A system is described for controlling wellbore drilling operations autonomously using satisficing parameters. The system can determine a wellbore-drilling envelope defining a zone for satisficed values of drilling parameters for a drilling operation. The system can receive real-time data for the drilling parameters and can compare the real-time data to the wellbore-drilling envelope. The system can output a command for automatically controlling the drilling operation in response to comparing the real-time data to the wellbore-drilling envelope.
E21B 44/00 - Automatic control systems specially adapted for drilling operations, i.e. self-operating systems which function to carry out or modify a drilling operation without intervention of a human operator, e.g. computer-controlled drilling systems; Systems specially adapted for monitoring a plurality of drilling variables or conditions
E21B 43/30 - Specific pattern of wells, e.g. optimizing the spacing of wells
2.
DETERMINING GAS-OIL AND OIL-WATER SHUT-IN INTERFACES FOR AN UNDULATING WELL
25 Abstract A system can determine a temperature profile based on a prior production temperature profile and a reference start point pressure for a well. The system can virtually divide the well into a plurality of sections including uphill sections and downhill sections. The system can determine a gas-oil interface depth for each section of the plurality of sections from a water-oil ratio and a gas-oil ratio based on the temperature profile and the reference start point pressure. The system can determine an oil-water interface depth for each section of the plurality of sections from the gas-oil ratio and the water-oil ratio based on the temperature profile and the reference start point pressure. Date Recue/Date Received 2021-05-18
A system includes equipment for at least one of formation of, stimulation of, or production from a wellbore, a processor, and a non-transitory memory device. The processor is communicatively coupled to the equipment. The non-transitory memory device contains instructions executable by the processor to cause the processor to perform operations comprising training a hybrid deep generative physics neural network (HDGPNN), iteratively computing a plurality of projected values for wellbore variables using the HDGPNN, comparing the projected values to measured values, adjusting the projected values using the HDGPNN until the projected values match the measured values within a convergence criteria to produce an output value for at least one controllable parameter, and controlling the equipment by applying the output value for the at least one controllable parameter.
E21B 41/00 - Equipment or details not covered by groups
E21B 43/00 - Methods or apparatus for obtaining oil, gas, water, soluble or meltable materials or a slurry of minerals from wells
E21B 44/00 - Automatic control systems specially adapted for drilling operations, i.e. self-operating systems which function to carry out or modify a drilling operation without intervention of a human operator, e.g. computer-controlled drilling systems; Systems specially adapted for monitoring a plurality of drilling variables or conditions
19 Abstract Aspects and features of a system for providing parameters for shale field configuration include a processor, and instructions that are executable by the processor. The system, using the processor, can receive resource supply data associated with a shale field to be penetrated by a wellbore or wellbores and simulate production from the shale field using the resource supply data to determine constraints and decision variables. The system can optimize a multi-objective function of the decision variables subject to the constraints to produce controllable parameters for operating the shale field. As examples, these parameters may be related to formation or stimulation of the wellbore or wellbores at the shale field site. Date Recue/Date Received 2021-03-09
System and methods for tuning equation of state (EOS) characterizations are presented. Pressure-volume-temperature (PVT) data is obtained for downhole fluids within a reservoir formation. A component grouping for an EOS model of the downhole fluids is determined, based on the obtained PVT data. The component grouping is used to estimate properties of the downhole fluids for a current stage of a downhole operation within the formation. A machine learning model is trained to minimize an error between the estimated properties and actual fluid properties measured during the current stage of the operation, where the component grouping for the EOS model is iteratively adjusted by the machine learning model until the error is minimized. The EOS model is tuned using the adjusted component grouping. Fluid properties are estimated for one or more subsequent stages of the downhole operation to be performed along the wellbore, based on the tuned EOS model.
A system can assign a value to one or more sustainability factors for a wellbore operation based on historical data. The system can determine, for each of the one or more sustainability factors, a weight. The system can determine a sustainability index corresponding to a predicted carbon footprint for the wellbore operation based on the weight and the value for each of the one or more sustainability factors. The system can output a command for adjusting the wellbore operation based on the sustainability index.
Aspects and features of a system for real-time drilling using automated data quality control can include a computing device, a drilling tool, sensors, and a message bus. The message bus can receive current data from a wellbore. The computing device can generate and use a feature-extraction model to provide revised data values that include those for missing data, statistical outliers, or both. The model can be used to produce controllable drilling parameters using highly accurate data to provide optimal control of the drilling tool. The real-time message bus can be used to apply the controllable drilling parameters to the drilling tool.
E21B 44/00 - Automatic control systems specially adapted for drilling operations, i.e. self-operating systems which function to carry out or modify a drilling operation without intervention of a human operator, e.g. computer-controlled drilling systems; Systems specially adapted for monitoring a plurality of drilling variables or conditions
A method for processing a well data log may comprise adding one or more boundary areas to the well data log, dividing the well data log into one or more segments using the one or more boundary areas, processing each of the one or more segments on one or more information handling systems, and reforming each of the one or more segments into a final simulation. A system for processing a well data log may comprise one or more information handling systems in a cluster. The one or more information handling systems may be configured to perform the method for processing the well data log.
A system includes a processor and a memory. The memory includes instructions that are executable by the processor to access training data of a modern feature of interest from direct observations, remotely determined data, or a combination thereof. The instructions are also executable to compile parameter data from at least one model simulation that impacts the modern feature of interest. The instructions are executable to train a machine-learning model to generate a predictive model that matches the training data of the modern feature of interest using the compiled parameter data as input. Furthermore, the instructions are executable to predict a feature of interest in a past time period using the predictive model and at least one historical model simulation that impacts the feature of interest. Additionally, the instructions are executable to execute a processing operation for facilitating hydrocarbon exploration based on the predicted feature of interest from the predictive model.
A system for determining exploration potential ranking for petroleum plays according to some aspects receives geological survey data of a geographical area to be ranked for a future petroleum play. The system generates predicted values based on the geological survey data, each predicted value indicating a probability that a portion of the basin includes a first characteristic. A set of polygons that represent the basin may be generated based on the predicted values. Each polygon represents a contiguous portion of the basin that has a same predicted value. A basin is score is generated by: generating a score for each polygon using the predicted value; and aggregating the score of each polygon of the set of polygons into the basin score. The basin score is displayed for use displaying for use in determining an area in which drilling a wellbore would have a greater probability of success.
A system for analysis and display of hydrocarbon play information according to some aspects determines a probability of source rock occurrence according to source rock age based on a proven play concept. The system can also determine a relative probability of migration for hydrocarbons from a source rock of a proposed petroleum play concept to a reservoir. A relative probability of wellbore success for the proposed play concept can be determined at least in part based on these probabilities. The system can display the relative probability of wellbore success for the proposed play concept, either alone as part of a displayed inventory of proposed hydrocarbon play concepts. The system can produce accurate results that facilitate rapid play concept investigations for hydrocarbon exploration.
A system includes a processor and a memory. The memory includes instructions that are executable by the processor to cause the processor to receive basin data of a target basin including an area of the target basin, a number of exploration wells in the target basin, and a number of discovery wells in the target basin. Additionally, the instructions are executable to cause the processor to provide the basin data as input to a trained machine- learning model to determine a predicted trajectory of exploration efficiency of the target basin. Further, the instructions are executable to cause the processor to, in response to providing the basin data as input to the trained machine-learning model, receive an output from the trained machine-learning model indicating the predicted trajectory of exploration efficiency in the target basin.
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
System for optimizing operation of an oil and gas well employs multi- objective Bayesian optimization of wellbore parameters to minimize scaling and corrosion. The system may contain instrumentation for measuring temperature, pressure, at least one production parameter and at least one ion concentration of the fluid in the wellbore. The system may also have a processor for performing a calculation procedure to determine an anticipated corrosion rate ("Vbase") and a scaling index ("Is") reflecting a tendency of scale to form in the wellbore based on the measurements provided by the instrumentation, where Vbase and Is are calculated along the length of the wellbore. Based on a selected set of optimization points taken from the calculations of Vbase and Is, the system may control the alkalinity and flow rate of the fluid based on the multi-objective optimization to simultaneously optimize scaling and corrosion.
Optimizing seismic to depth conversion to enhance subsurface operations including measuring seismic data in a subsurface formation, dividing the subsurface formation into a training area and a study area, dividing the seismic data into training seismic data and study seismic data, wherein the training seismic data corresponds to the training area, and wherein the study seismic data corresponds to the study area, calculating target depth data corresponding to the training area, training a machine learning model using training inputs and training targets, wherein the training inputs comprise the training seismic data, and wherein the training targets comprise the target depth data, computing, by the machine learning model, output depth data corresponding to the study area based at least in part on the study seismic data; and modifying one or more subsurface operations corresponding to the study area based at least in part on the output depth data.
Aspects and features of this disclosure relate to projecting physical drilling parameters to control a drilling operation. A computing system applies Bayesian optimization to a model incorporating the input data using varying values for an adverse drilling factor to produce a target function. The computing system determines a minimum value for the target function. The computing system provides a projected value for the physical drilling parameters based on the minimum value. The computing system generates an alert responsive to determining that the projected value for the physical drilling parameters exceeds a prescribed limit.
E21B 44/00 - Automatic control systems specially adapted for drilling operations, i.e. self-operating systems which function to carry out or modify a drilling operation without intervention of a human operator, e.g. computer-controlled drilling systems; Systems specially adapted for monitoring a plurality of drilling variables or conditions
ABSTRACT A method for designing a borehole tubular for use in a borehole. The method may include defining tubular sections that make up the borehole tubular, defining a downhole operation that will be conducted using the borehole tubular at a first timestamp, determining loads that will be applied to each of the tubular sections at respective specific depths along the borehole during the downhole operation at the first timestamp, determining a design limit envelope for each of the tubular sections at the first timestamp based on design parameters of the tubular section and the specific depth of the tubular section at the first timestamp, and displaying a three-dimensional (3D) plot of the design limit envelopes of the tubular sections and the loads applied to the tubular sections as a function of depth within the borehole on a display. 36 Date Recue/Date Received 2020-12-23
Hydrocarbon exploration and extraction can be facilitated using machine- learning models. For example, a system described herein can receive seismic data indicating locations of geological bodies in a target area of a subterranean formation. The system can provide the seismic data as input to a trained machine-learning model for determining whether the target area of the subterranean formation includes one or more types of geological bodies. The system can receive an output from the trained machine- learning model indicating whether or not the target area of the subterranean formation includes the one or more types of geological bodies. The system can then execute one or more processing operations for facilitating hydrocarbon exploration or extraction based on the seismic data and the output from the trained machine-learning model.
G01V 1/36 - Effecting static or dynamic corrections on records, e.g. correcting spread; Correlating seismic signals; Eliminating effects of unwanted energy
18.
EARLY WARNING AND AUTOMATED DETECTION FOR LOST CIRCULATION IN WELLBORE DRILLING
A wellbore drilling system can generate a machine-learning model trained using historic drilling operation data for monitoring for a lost circulation event. Real-time data for a drilling operation can be received and the machine-learning model can be applied to the real-time data to identify a lost circulation event that is occurring. An alarm can then be outputted to indicate a lost circulation event is occurring for the drilling operation.
This disclosure presents methods and systems to perform fairway analysis on a computing system to automate tasks. The automation of the fairway analysis can reduce bias and uncertainty introduced by a user using their own set of assumptions, estimations, and preferred sequencing of rules and algorithms. The described processes can receive initial input parameters describing the area of interest (A0I) and a geological age range. The processes can retrieve appropriate geological and stratigraphic parameters using the initial input parameters. The combined input parameters can then be geoprocessed using age-aware rules and a determined sequence of algorithms and rules to generate synthesized geological data that can be upscaled and transformed into one or more chance maps indicating the presence and effectiveness of various hydrocarbon elements. The chance maps can be amalgamated and processed to produce a prospective map indicating the likelihood of success of further exploration of the specified AOI.
System and methods for event prediction during drilling operations are provided. Regression data associated with coefficients of a predictive model are retrieved for a downhole event during a drilling operation along a planned path of a wellbore. The regression data includes a record of changes in historical coefficient values associated with prior occurrences of the event. As the wellbore is drilled over different stages of the operation, a value of an operating variable is estimated based on values of the coefficients and real-time data acquired during each stage. A percentage change in coefficient values adjusted between successive stages of the operation is tracked. An occurrence of the downhole event is estimated, based on a correlation between the percentage change tracked for at least one coefficient and a corresponding change in the historical coefficient values. The path of the wellbore is adjusted, based on the estimated occurrence of the event.
E21B 44/00 - Automatic control systems specially adapted for drilling operations, i.e. self-operating systems which function to carry out or modify a drilling operation without intervention of a human operator, e.g. computer-controlled drilling systems; Systems specially adapted for monitoring a plurality of drilling variables or conditions
E21B 21/08 - Controlling or monitoring pressure or flow of drilling fluid, e.g. automatic filling of boreholes, automatic control of bottom pressure
21.
TECHNIQUES FOR EXTRACTION OF VECTORIZED CONTENT OF AN OIL AND GAS PLAY WITHIN AN UNSTRUCTURED FILE
20 Abstract A method includes retrieving an unstructured document and defining an area of interest of the unstructured document that visually represents geological formation information. The method also includes extracting a set of vectorized polygons from the area of interest. Additionally, the method includes assigning properties from the unstructured document to each of the vectorized polygons in the set of vectorized polygons. Further, the method includes assigning a coordinate reference frame to the set of vectorized polygons and generating a user-interactive document from the set of vectorized polygons. Date RecuelDate Received 2020-12-14
Docket No.: 2019-1PM-103336 Ul PCT 10090PCT-631617 ABSTRACT Aspects of the disclosed technology provide techniques for determining frictional forces bearing on a downhole drill string. In some implementations, a method of the disclosed technology can include steps for segmenting a plurality of continuous nodes of the drilling string into a first segment and a second segment, computing a first set of values corresponding with one or more nodes in the first segment using a first model, computing a second set of values corresponding with one or more nodes in the second segment using a second model, and determining a torque of the drill string based on the first set of values and the second set of values. In some aspects, the method can further include steps for determining a drag force on the drill string based on the first set of values and the second set of values. Systems and machine- readable media are also provided. 20 of 20 Date Recue/Date Received 2020-11-26
Gas bubble migration can be managed in liquids. In one example, a system can execute wellbore-simulation software to simulate changes in gas dissolution in a liquid over time. This may involve dividing the wellbore into segments spanning from the well surface to the downhole location, each segment spanning a respective depth increment between the well surface and the downhole location. Next, for each time, the system can determine a respective multiphase-flow regime associated with each segment of the plurality of segments based on a simulated pressure level, a simulated temperature, a simulated pipe eccentricity, and a simulated fluid velocity at the segment. The system can also determine how much of the gas is dissolved in the liquid at each segment based on the respective multiphase-flow regime at the segment. The system can display a graphical user interface representing the gas dissolution in the liquid over time.
A method for creating a seamless scalable geological model may comprise identifying one or more geological scales, establishing a geological tied system, identifying one or more graphical resolution levels for each of the one or more geological scales, constructing the seamless scalable geological model, and producing a post-process model. A system for creating a seamless scalable geological model may comprise an information handling system, which may comprise a random access memory, a graphics module, a main memory, a secondary memory, and one or more processors configured to run a seamless scalable geological model software.
A method for identifying a flow parameter in a wellbore may comprise identifying a state vector at a moment t, performing a flow simulation using a flow model, predicting the state vector and a covariance matrix at the moment t, updating the state vector with an EnKF algorithm, correcting the state vector at the moment t, and updating the flow simulation model. A system for identifying a flow parameter in a wellbore may comprise a distributed acoustic system into a wellbore and an information handling system. The distributed acoustic system may comprise a fiber optic cable and at least one measurement device.
A system for determining real time cluster efficiency for a pumping operation in a wellbore includes a pump, a surface sensor, a downhole sensor system, and a computing device. The pump can pump slurry or diverter material in the wellbore. The surface sensor can be positioned at a surface of the wellbore to detect surface data about the pump. The downhole sensor system can be positioned in the wellbore to detect downhole data about an environment of the wellbore. The computing device can receive the surface data from the surface sensor, receive the downhole data from the downhole sensor system, apply the surface data and the downhole data to a long short-term memory (LSTM) neural network to produce a predicted cluster efficiency associated with operational settings of the pump, and control the pump using the operational settings to achieve the predicted cluster efficiency.
E21B 43/16 - Enhanced recovery methods for obtaining hydrocarbons
E21B 47/135 - Means for transmitting measuring-signals or control signals from the well to the surface, or from the surface to the well, e.g. for logging while drilling by electromagnetic energy, e.g. of radio frequency range using light waves, e.g. infrared or ultraviolet waves
E21B 43/12 - Methods or apparatus for controlling the flow of the obtained fluid to or in wells
E21B 43/26 - Methods for stimulating production by forming crevices or fractures
A method for determining validity of an arrangement of a plurality of contiguous depositional environment polygons that depict a geological region includes obtaining a plurality of polygons stored in a dataset of polygons, each polygon of the plurality of polygons assigned a different depositional environment characteristic, and the plurality of polygons representing the geological region, arranging the plurality of polygons adjacent each other using geographical coordinates of the polygons, and thereby obtain an arrangement of the plurality of polygons, determining whether the arrangement of the plurality of polygons is valid using a rule-base that includes pairs of compatible depositional environment characteristics arranged adjacent each other, and generating a map of geological polygons for a region that facilitates exploration of a natural resource
A system and method can be used for to calibrating time-lapse seismic volumes by cross-migration rescaling and reorientation for use in determining optimal wellbore placement or production in a subsurface environment. Certain aspects include methods for cross-migration of data sets processed using different migration techniques. Pre-processing of the data sets, optimization of rescaling and reorientation, and identification of adjustment parameters associated with minimum global error can be used to achieve a time-dependent formation data set that addresses error in all input data sets.
A system includes a processing device and a non-transitory computer-readable medium having instructions stored thereon that are executable by the processing device to cause the system to perform operations. The operations include generating and running a reservoir simulation model. The reservoir and simulation model includes representative natural fracture or secondary porosity attributes for an area of interest for one or more wells. The operations also include generating a synthetic G-function response using results of the reservoir simulation model. Additionally, the operations include calibrating the synthetic G-function response from the reservoir simulation model to a field G-function response generated using results of a field diagnostic fracture injection test by changing natural fracture characteristics of the reservoir simulation model. Further, the operations include formulating a drilling plan, a completion plan, or both for a wellbore in the area of interest using the synthetic G-function response.
E21B 49/00 - Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells
E21B 43/26 - Methods for stimulating production by forming crevices or fractures
30.
AUTOMATED RATE OF PENETRATION OPTIMIZATION FOR DRILLING
Systems and methods for controlling drilling operations are provided. A controller for a drilling system may provide drilling parameters such as weight-on-bit and rotation rate parameters to the drilling system, based on a machine-learned reward policy and a model-based prediction. The machine-learned reward policy may be generated during drilling operations and used to modify recommended values from the model-based prediction for subsequent drilling operations to achieve a desired rate-of-penetration.
E21B 44/00 - Automatic control systems specially adapted for drilling operations, i.e. self-operating systems which function to carry out or modify a drilling operation without intervention of a human operator, e.g. computer-controlled drilling systems; Systems specially adapted for monitoring a plurality of drilling variables or conditions
G06N 99/00 - Subject matter not provided for in other groups of this subclass
31.
AUTOMATED PRODUCTION HISTORY MATCHING USING BAYESIAN OPTIMIZATION
A history-matched oilfield model that facilitates well system operations for an oilfield is generated using a Bayesian optimization of adjustable parameters based on an entire production history. The Bayesian optimization process includes stochastic modifications to the adjustable parameters based on a prior probability distribution for each parameter and a model error generated using historical production measurement values and corresponding model prediction values for various sets of test parameters.
System and methods for simulating fluid flow during downhole operations are provided. Measurements of an operating variable at one or more locations within a formation are obtained from a downhole tool disposed in a wellbore within the formation during a current stage of a downhole operation being performed along the wellbore. The obtained measurements are applied as inputs to a hybrid model of the formation. The hybrid model includes physics-based and machine learning models that are coupled together within a simulation grid. Fluid flow within the formation is simulated, based on the inputs applied to the hybrid model. A response of the operating variable is estimated for a subsequent stage of the downhole operation along the wellbore, based on the simulation. Flow control parameters for the subsequent stage are determined based on the estimated response. The subsequent stage of the operation is performed according to the determined flow control parameters.
Systems and methods for cloud-based management of reservoir simulation projects are provided. A cloud-based application server may receive from a client device over the communication network information defining a reservoir simulation project for a wellsite in a hydrocarbon producing field. The reservoir simulation project may include at least one reservoir simulation job to be performed by the cloud-based application server. The information may include one or more parameters for the reservoir simulation job. The cloud-based application server may perform the reservoir simulation job according to the one or more parameters. The cloud-based application server may provide results of the simulation job to the client device over the communication network for display within a graphical user interface (GUI) provided at the client device for a cloud-based reservoir simulation application executable by the application server.
Managing execution of a workflow has a set of subworkflows. Optimizing the set of subworkflows using a deep neural network, each subworkflow of the set has a set of tasks. Each task of the sets has a requirement of resources of a set of resources; each task of the sets is enabled to be dependent on another task of the sets of tasks. Training the deep neural network by: executing the set of subworkflows, collecting provenance data from the execution, and collecting monitoring data that represents the state of said set of resources. Training causes the neural network to learn relationships between the states of the set of resources, the sets of tasks, their parameters and the obtained performance. Optimizing an allocation of resources to each task to ensure compliance with a user-defined quality metric based on the deep neural network output.
G06Q 10/0631 - Resource planning, allocation, distributing or scheduling for enterprises or organisations
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
G06F 9/50 - Allocation of resources, e.g. of the central processing unit [CPU]
A system and method for controlling a gas supply to provide gas lift for a production wellbore makes use of Bayesian optimization. A computing device controls a gas supply to inject gas into one or more wellbores. The computing device receives reservoir data associated with a subterranean reservoir to be penetrated by the wellbores and can simulate production using the reservoir data and using a physics-based or machine learning or hybrid physics-based machine learning model for the subterranean reservoir. The production simulation can provide production data. A Bayesian optimization of an objective function of the production data subject to any gas injection constraints can be performed to produce gas lift parameters. The gas lift parameters can be applied to the gas supply to control the injection of gas into the wellbore or wellbores.
A device comprises a processor; and a memory device including instructions that, when executed by the processor, cause the processor to: obtain, from a server, a plate model, wherein the plate model includes a plurality of geodynamic units (GDUs) representing a plurality of different geological regions; receive a user-defined geospatial data of a desired geological region; perform an intersection operation between the user-defined geospatial data and the plurality of GDUs of the plate model, to assign user-defined geospatial data a GDU identifier; obtain, from a server, Euler rotation poles based on a user-specified geological age, each Euler rotation pole being associated with a GDU via the GDU identifier; and reconstruct the user-defined geospatial data to the geological age using the Euler rotation pole and thereby obtain a reconstructed paleogeographic position of the user- defined geospatial data.
Aspects of the present disclosure relate to projecting control parameters of equipment associated with forming a wellbore, stimulating the wellbore, or producing fluid from the wellbore. A system includes the equipment and a computing device. The computing device is operable to project a control parameter value of the equipment using an equipment control process, and to receive confirmation that the projected control parameter value is within an allowable operating range. The computing device is also operable to adjust the equipment control process based on the confirmation, and to control the equipment to operate at the projected control parameter value. Further, the computing device is operable to receive real-time data associated with the forming of the wellbore, the stimulating of the wellbore, or the producing fluid from the wellbore. Furthermore, the computing device is operable to adjust the equipment control process based on the real-time data.
E21B 44/00 - Automatic control systems specially adapted for drilling operations, i.e. self-operating systems which function to carry out or modify a drilling operation without intervention of a human operator, e.g. computer-controlled drilling systems; Systems specially adapted for monitoring a plurality of drilling variables or conditions
38.
DISTRIBUTED CONTROL SYSTEM USING ASYNCHRONOUS SERVICES IN A WELLBORE
Certain aspects and features relate to a system that efficiently determines optimal actuator set points to satisfy an objective in controlling equipment such as systems for drilling, production, completion or other operations associated with oil or gas production from a wellbore. A platform can receive data and also make use of and communicate with multiple algorithms asynchronously and efficiently to project automatic optimum set points for controllable parameters. Services can provide data over a real-time messaging bus and the data can be captured by an orchestrator that aggregates all data and calls a solver orchestrator to determine optimized parameters for a current state in time to send to control systems or display in a dashboard.
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
E21B 41/00 - Equipment or details not covered by groups
Multiple projected rate of penetration (ROP) values can be determined for purposes of adjusting well tools and well characteristics. For example, surface data can be determined based on a surface sensor signal. Downhole data can be determined based on a downhole sensor signal. A first value indicating a first projected ROP of a drill bit can be determined by providing the surface data as input to a first machine-learning model. A second value indicating a second projected ROP of the drill bit can be determined by providing the downhole data as input to a second machine-learning model. A third value indicating a third projected ROP of the drill bit can be determined by providing the first value and the second value input to a third machine-learning model. An operating characteristic of a well tool can be adjusted based on the third value.
E21B 44/00 - Automatic control systems specially adapted for drilling operations, i.e. self-operating systems which function to carry out or modify a drilling operation without intervention of a human operator, e.g. computer-controlled drilling systems; Systems specially adapted for monitoring a plurality of drilling variables or conditions
E21B 41/00 - Equipment or details not covered by groups
40.
DRILL BIT SUBSYSTEM FOR AUTOMATICALLY UPDATING DRILL TRAJECTORY
A drill bit subsystem can include a drill bit, a processor, and a non-transitory computer-readable medium for storing instructions and for being positioned downhole with the drill bit. The instructions of the non-transitory computer-readable medium can include a machine-teachable module and a control module that are executable by the processor. The machine-teachable module can receive depth data and rate of drill bit penetration from one or more sensors in a drilling operation, and determine an estimated lithology of a formation at which the drill bit subsystem is located. The control module can use the estimated lithology to determine an updated location of the drill bit subsystem, and control a direction of the drill bit using the updated location and a drill plan.
E21B 44/00 - Automatic control systems specially adapted for drilling operations, i.e. self-operating systems which function to carry out or modify a drilling operation without intervention of a human operator, e.g. computer-controlled drilling systems; Systems specially adapted for monitoring a plurality of drilling variables or conditions
E21B 41/00 - Equipment or details not covered by groups
E21B 49/00 - Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells
CA 03093530 2020-09-09 (12) INTERNATIONAL APPLICATION PUBLISHED UNDER THE PATENT COOPERATION TREATY (PCT) (19) World Intellectual Property Organization 1 11111 1 111111 11 111111 1 11 11111 1 111 1111 1 1 1 1 1 111 111 111 111111 111 111 1111111111 1 11 1111 International Bureau (10) International Publication Number (43) International Publication Date WO 2019/236078 Al 12 December 2019 (12.12.2019) WIPO I PCT (51) International Patent Classification: CA, CH, CL, CN, CO, CR, CU, CZ, DE, DJ, DK, DM, DO, G01V 99/00 (2009.01) G16C 10/00 (2019.01) DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, HN, G01N 33/24 (2006.01) HR, HU, ED, EL, IN, IR, IS, JO, JP, KE, KG, KH, KN, KP, KR, KW, KZ, LA, LC, LK, LR, LS, LU, LY, MA, MD, ME, (21) International Application Number: MG, MK, MN, MW, MX, MY, MZ, NA, NG, NI, NO, NZ, PCT/U52018/036308 OM, PA, PE, PG, PH, PL, PT, QA, RO, RS, RU, RW, SA, (22) International Filing Date: SC, SD, SE, SG, SK, SL, SM, ST, SV, SY, TH, TJ, TM, TN, 06 June 2018 (06.06.2018) TR, TT, TZ, UA, UG, US, UZ, VC, VN, ZA, ZM, ZW. (25) Filing Language: English (84) Designated States (unless otherwise indicated, for every kind of regional protection available): ARIPO (BW, GH, (26) Publication Language: English GM, KE, LR, LS, MW, MZ, NA, RW, SD, SL, ST, SZ, TZ, (71) Applicant: LANDMARK GRAPHICS CORPO- UG, ZM, ZW), Eurasian (AM, AZ, BY, KG, KZ, RU, TJ, RATION [US/US]; 3000 N. Sam Houston Pkwy E., Hous- TM), European (AL, AT, BE, BG, CH, CY, CZ, DE, DK, ton, TX '77032-3219 (US). EE, ES, FI, FR, GB, GR, HR, HU, EE, IS, IT, LT, LU, LV, MC, MK, MT, NL, NO, PL, PT, RO, RS, SE, SI, SK, SM, (72) Inventors: BAINES, Graham; 31 Wilsham Rd., Abing- TR), OAPI (BF, BJ, CF, CG, CI, CM, GA, GN, GQ, GW, don 0X14 5LD (GB). SAUNDERS, Benjamin S.; 3 Anvil KM, ML, MR, NE, SN, TD, TG). Court, Stanford In The Vale SN7 8NN (GB). NICOLL, Graeme Richard; 6 White Road, East Hendred 0X12 8JG Declarations under Rule 4.17: (GB). WROBEL-DAVEAU, Jean-Christophe; 12 Bec ¨ of inventorship (Rule 4.17(iv)) Close, Wantage 0X12 9EP (GB). Published: (74) Agent: LI, William et al.; McGuireWoods LLP, 1750 ¨ with international search report (Art. 21(3)) Tysons Blvd, Suite 1800, Tysons Corner, VA 22102 (US). (81) Designated States (unless otherwise indicated, for every kind of national protection available): AE, AG, AL, AM, AO, AT, AU, AZ, BA, BB, BG, BH, BN, BR, BW, BY, BZ, (54) Title: GEOLOGICAL DATA ASSESSMENT SYSTEM (57) Abstract: The disclosed embodiments include systems and methods to as- 100 sess geological data. The method includes obtaining data associated with a geo- ( START ) logical state of a geological entity. The method also includes assessing a nature of a geological age constraint of the geological entity. The method further includes = s102-, OBTAIN DATA ASSOCIATED WITH A generating a first probability distribution of a geological age of the geological GEOLOGICAL STATE OF A GEOLOGICAL ENTITY entity based on the nature of the geological age constraint of the geological entity. S1041 ASSESS A NATURE OF A GEOLOGICAL AGE The method further includes selecting a time of interest for analysis of the geo- CONSTRAINT OF THE GEOLOGICAL ENTITY logical entity. The method further includes assessing a nature of the geological age constraint during the time of interest. The method further includes generating GENERATE A FIRST PROBABILITY DISTRIBUTION OF A GEOLOGICAL AGE OF a second probability distribution for the time of interest. The method further in- S106-, THE GEOLOGICAL ENTITY BASED ON THE cludes determining a likelihood that the geological age constraint of the geologi- NATURE OF THE GEOLOGICAL AGE CONSTRAINT OF THE GEOLOGICAL ENTITY cal entity coincides with the time of interest. s1081 SELECT A TIME OF INTEREST FOR ANALYSIS OF THE GEOLOGICAL ENTITY ASSESS A NATURE OF THE GEOLOGICAL AGE $112 CONSTRAINT DURING THE TIME OF INTEREST GC GENERATE A SECOND PROBABILITY S114 --" DISTRIBUTION FOR THE TIME OF INTEREST DETERMINE A LIKELIHOOD THAT THE GEOLOGICAL AGE CONSTRAINT OF THE GEOLOGICAL ENTITY COINCIDES WITH THE TIME OF INTEREST ( END ) FIG. 1
G16C 10/00 - Computational theoretical chemistry, i.e. ICT specially adapted for theoretical aspects of quantum chemistry, molecular mechanics, molecular dynamics or the like
CA 03090956 2020-08-11 (12) INTERNATIONAL APPLICATION PUBLISHED UNDER THE PATENT COOPERATION TREATY (PCT) (19) World Intellectual Property 1 11111 1 111111 11 111111 1 11 11111 1 111 1111 1 1 1 1 1 111 1 111 1 1111 111 1 1111 11 1111111111 1 11 1111 Organization International Bureau (10) International Publication Number (43) International Publication Date WO 2019/221717 Al 21 November 2019 (21.11.2019) WIPO I PCT (51) International Patent Classification: RA, Harsh Biren; 410 Avondale Street, Houston, Texas E21B 41/00 (2006.01) GOON 3/04 (2006.01) 77006 (US). GOON 3/08 (2006.01) (74) Agent: RAJ, Vinu et al.; Haynes and Boone, LLP, 2323 (21) International Application Number: Victory Avenue, Suite 700, Dallas, TX 75219 (US). PCT/U52018/032816 (81) Designated States (unless otherwise indicated, for every (22) International Filing Date: kind of national protection available): AE, AG, AL, AM, 15 May 2018 (15.05.2018) AO, AT, AU, AZ, BA, BB, BG, BH, BN, BR, BW, BY, BZ, CA, CH, CL, CN, CO, CR, CU, CZ, DE, DJ, DK, DM, DO, (25) Filing Language: English DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, HN, (26) Publication Language: English HR, HU, ED, EL, IN, IR, IS, JO, JP, KE, KG, KH, KN, KP, (71) Applicant: LANDMARK GRAPHICS CORPO- KR, KW, KZ, LA, LC, LK, LR, LS, LU, LY, MA, MD, ME, MG MK, MN, 1VIW, MX, MY MZ NA NG, NI, NO, NZ RATION [US/US]; 3000 N. Sam Houston Parkway E., OM, , PA, PE, PG, PH, PL, PT,, QA ,, RO ,, RS, RU, RW, SA,, Houston, Texas 77032-3219 (US). SC, SD, SE, SG, SK, SL, SM, ST, SV, sy TH, TJ, TM TN, (72) Inventors: MADASU, Srinath; 11319 Briarforest Drive, TR, TT, TZ, UA, UG, US, UZ, VC, VN, ZA, ZM, ZW. Houston, Texas 77077 (11S). ZAGAYEVSKIY, Yevgeniy; (84) Designated States (unless otherwise indicated, for every 3788 Richmond Ave., Apt. 1362, Houston, Texas 77046 kind of regional protection available): ARIPO (BW, GH, (US). WONG, Terry; 21525 Spring Plaza Drive, Spring, GM, KE, LR, LS, MW, MZ, NA, RW, SD, SL, ST, SZ, TZ, Texas 77388 (US). CAMILLERI, Dominic; 20006 Sky UG, ZM ZW), Eurasian (AM AZ, BY, KG, KZ, RU, TJ, Hollow Ln, Katy, Texas T7450 (US). WANG, Charles Hai; TM), European (AL, AT, BE, BG, CH, CY, CZ, DE, DK, 5925 Almed Rd., Unit 11206, Houston, Texas T7004 (US). EE, ES, FL FR, GB, GR, HR, HU, 11E, IS, IT, LT, LU, LV, BECK, Courtney Leeann; 600 Studemont Street 43406, MC, MK, MT, NL, NO, PL, PT, RO, RS, SE, SI, SK, SM, Houston, Texas 77007 (US). MAO, Hanzi; 1800 Holleman TR), OAPI (BF, BJ, CF, CG, CI, CM GA, GN, GQ, GW, Dr., Apt. 422, College Station, Texas T7840 (US). DONG, KM, ML, MR, NE, SN, TD, TG). Hui; 3543 Greystone Dr., Austin, Texas 78731 (US). VO- (54) Title: PETROLEUM RESERVOIR BEHAVIOR PREDICTION USING A PROXY FLOW MODEL (57) Abstract: Using production data and a production flow record based on the production . data, a deep neural network (DNN) is trained to model a proxy flow simulation of a reser- 100 voir. The proxy flow simulation of the reservoir is performed, using an ensemble Kalman filter 171: (EnKF), based on the trained DNN. The EnKF assimilates new data through updating a current . _ ensemble to obtain history matching by minimizing a difference between a predicted produc- i.., . tion output from the proxy flow simulation and measured production data from a field. Using 128 the updated current ensemble, a second proxy flow simulation of the reservoir is performed based on the trained DNN. The assimilating and the performing are repeated while new data is available for assimilating. Predicted behavior of the reservoir is determined based on the "*H 71=41 1 132 pro flow simulation of the reservoir. An indication of the predicted behavior is provided to . )cc=b7.,."==4; xy :..! facilitate production of fluids from the reservoir. ¨ = . . ,= te= . -.0!C 1181, - =._.28 1i6- " = 120 1 _ 125 "1:;,) ''`r:3=125 124* FIG. 1 [Continued on next page] CA 03090956 2020-08-11 WO 2019/221717 Al I 11111 I 011111 II 111111 0 11111111 0 Ill 1111 I 01110 Ill 0 Ill 0 Ill 11 111 010 I II 11111111111 0 II 1111 Declarations under Rule 4.17: ¨ as to applicant's entitlement to apply for and be granted a patent (Rule 4.1700) Published: ¨ with international search report (Art. 21(3))
An information processing system having a processor and a memory device coupled to the processor, wherein the memory device includes a set of instruction that, when executed by the processor, cause the processor to receive a multi-dimensional grid of acoustic or elastic impedances determined from seismic survey data associated with a subterranean formation, receive elastic property data that describes elastic property characteristics used to sort pseudo-components, and wherein the respective pseudo-components are formed of a combination of two or more lithologies. The instructions, when executed by the processor, further cause the processor to define select design variables using the impedance arrays, perform optimization operations for optimizing select design variables by applying the elastic property data as a part of a constitutive relation, and output a distribution of the pseudo- components to characterize volumetric concentrations of spatially grouped lithologies in a control volume of the subterranean formation.
A method for optimizing real time drilling with learning uses a multi-layer Deep Neural Network (DNN) built from input drilling data. A plurality of drilling parameter features is extracted using the DNN. A linear regression model is built based on the extracted plurality of drilling parameter features. The linear regression model is applied to predict one or more drilling parameters.
E21B 44/00 - Automatic control systems specially adapted for drilling operations, i.e. self-operating systems which function to carry out or modify a drilling operation without intervention of a human operator, e.g. computer-controlled drilling systems; Systems specially adapted for monitoring a plurality of drilling variables or conditions
E21B 41/00 - Equipment or details not covered by groups
45.
METHOD FOR GENERATING PREDICTIVE CHANCE MAPS OF PETROLEUM SYSTEM ELEMENTS
The disclosure generally relates to generating chance maps. More specifically, the disclosure relates to a method for generating predictive chance maps illustrating the potential presence of petroleum systems elements at a geographical location from Earth system models. When exploring for hydrocarbons in frontier basins or plays, being able to meaningfully predict the presence and quality of petroleum systems elements is important. Traditionally Earth system models have been used to assist this prediction by generating definitive "yes" or "no" predictions about the presence of the petroleum systems elements. That is, the methods provide a "drill here" solution for an operator to encounter source rocks. These traditional methods often fail to account for uncertainties in the model inputs and Earth system simulations. Without accounting for such uncertainties, the traditional methods offer an operator a false level of precision with the predictions. Further, the "yes" or "no" definitive predictions do not provide a prediction that is consistent with the level of precision permitted by the modeling technique.
A method for history matching a reservoir model based on actual production data from the reservoir over time generates an ensemble of reservoir models using geological data representing petrophysical properties of a subterranean reservoir. Production data corresponding to a particular time instance is acquired from the subterranean reservoir. Normal score transformation is performed on the ensemble and on the acquired production data to transform respective original distributions into normal distributions. The generated ensemble is updated based on the transformed acquired production data using an ensemble Kalman filter (EnKF). The updated generated ensemble and the transformed acquired production data are transformed back to respective original distributions. Future reservoir behavior is predicted based on the updated ensemble.
Disclosed are systems and methods for allocating resources for executing a simulation. These include receiving a simulation for execution, calculating an initial runtime of an initial time step of the simulation, determining a total runtime of the simulation based on the initial runtime, selecting a runtime model based on the initial time step, total runtime, or a parameter of the simulation, identifying, based on the selected runtime model, an allocation of a resource providing an increase in runtime speed, allocating the identified resource, and executing the simulation using the allocated resource.
A method for fracturing a formation is provided. Real-time fracturing data is acquired from a well bore during fracturing operation. The real-time fracturing data is processed using a recurrent neural network trained using historical data from analogous wells. A real-time response variable prediction is determined using the processed real-time fracturing data. Fracturing parameters for the fracturing operation are adjusted in real-time based on the real-time response variable prediction. The fracturing operation is performed using the fracturing parameters that were adjusted based on the real-time response variable prediction.
E21B 43/26 - Methods for stimulating production by forming crevices or fractures
G06N 3/044 - Recurrent networks, e.g. Hopfield networks
G06N 3/0442 - Recurrent networks, e.g. Hopfield networks characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
49.
USING EXISTING SERVERS IN A WELLBORE ENVIRONMENT AS DATA SOURCES FOR STREAMING SERVERS
A streaming server can receive a request from a client device to access data about a wellbore environment in a database server. The database server can be communicatively coupled to a server, which can be communicatively coupled to the streaming server. The streaming server can communicate data in a standardized format with the server using a request and response protocol. The streaming server can communicate the wellbore environment data from the database server in a streaming format with the client device.
H04L 12/28 - Data switching networks characterised by path configuration, e.g. LAN [Local Area Networks] or WAN [Wide Area Networks]
H04L 67/02 - Protocols based on web technology, e.g. hypertext transfer protocol [HTTP]
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
H04L 67/133 - Protocols for remote procedure calls [RPC]
H04L 69/08 - Protocols for interworking; Protocol conversion
E21B 41/00 - Equipment or details not covered by groups
A system and method for controlling a drilling tool inside a wellbore makes use of Bayesian optimization with range constraints. A computing device samples observed values for controllable drilling parameters such as weight-on-bit (WOB) and drill bit rotational speed in RPM and evaluates a selected drilling parameter such a rate-of-penetration (ROP) for the observed values using an objective function. Range constraints can be continuously learned by the computing device as the range constraints change. A Bayesian optimization, subject to the range constraints and the observed values, can produce an optimized value for the controllable drilling parameter to achieve a predicted value for the selected drilling parameter. The system can then control the drilling tool using the optimized value to achieve the predicted value for the selected drilling parameter.
E21B 44/00 - Automatic control systems specially adapted for drilling operations, i.e. self-operating systems which function to carry out or modify a drilling operation without intervention of a human operator, e.g. computer-controlled drilling systems; Systems specially adapted for monitoring a plurality of drilling variables or conditions
E21B 41/00 - Equipment or details not covered by groups
51.
SEISMIC ROCK PROPERTY PREDICTION IN FORWARD TIME BASED ON 4D SEISMIC ANALYSIS
System and methods for predicting time-dependent rock properties are provided. Seismic data for a subsurface formation is acquired over a plurality of time intervals. A value of at least one rock property of the subsurface formation is calculated for each of the plurality of time intervals, based on the corresponding seismic data acquired for that time interval. At least one of a trend or a spatio-temporal relationship in the seismic data is determined based on the value of the at least one rock property calculated for each time interval. A value of the at least one rock property is estimated for a future time interval, based on the determination. The estimated value of the at least one rock property is used to select a location for a wellbore to be drilled within the subsurface formation. The wellbore is then drilled at the selected location.
An estimated gas leak flow rate can be determined using a teaching set of concentration profiles, a regression model implemented by a machine-learning subsystem, and a subset of attributes measured within an environment. The teaching set of concentration profiles can include gas flow rates associated with relevant attributes. The regression model can be transformed into a gas leak flow regression model via the machine-learning subsystem using the teaching set. The subset of attributes measured within the environment can be applied to the gas leak flow regression model to determine other attributes absent from the subset of attributes and an estimated gas flow rate for the environment. A gas leak attenuation action can be performed in response to the estimated gas flow rate.
G01M 3/26 - Investigating fluid tightness of structures by using fluid or vacuum by measuring rate of loss or gain of fluid, e.g. by pressure-responsive devices, by flow detectors
53.
EFFECTIVE REPRESENTATION OF COMPLEX THREE-DIMENSIONAL SIMULATION RESULTS FOR REAL-TIME OPERATIONS
System and methods for training neural network models for real-time flow simulations are provided. Input data is acquired. The input data includes values for a plurality of input parameters associated with a multiphase fluid flow. The multiphase fluid flow is simulated using a complex fluid dynamics (CFD) model, based on the acquired input data. The CFD model represents a three-dimensional (3D) domain for the simulation. An area of interest is selected within the 3D domain represented by the CFD model. A two-dimensional (2D) mesh of the selected area of interest is generated. The 2D mesh represents results of the simulation for the selected area of interest. A neural network is then trained based on the simulation results represented by the generated 2D mesh.
G06F 30/28 - Design optimisation, verification or simulation using fluid dynamics, e.g. using Navier-Stokes equations or computational fluid dynamics [CFD]
54.
GRIDDING GLOBAL DATA INTO A MINIMALLY DISTORTED GLOBAL RASTER
Map projections necessarily distort the Earth's surface in some fashion as a result of the transformation to a coordinate system. However, different map projection systems can preserve some properties of geospatial data (e.g., area) at the expense of other properties (e.g., distance or azimuth). To produce a minimally distorted global raster, a global raster generator creates a number and variety of projections using as input geospatial data. The generator intelligently selects the projection systems based on properties of the input data and desired properties of an output global raster. The generator then applies interpolation algorithms to the projections to produce smooth and continuous projections. The generator then re-projects the interpolated projections to a desired output projection system and filters the projections to identify and remove regions of the projections which exhibit distortion. The generator merges the filtered projections which results in a minimally distorted global raster.
The disclosure provides a method of generating a basin fill model using a set of known paleogeographic characteristic parameters, for a specified basin location and time interval. The basin fill model can be used to assist in predicting the location of submarine fan deposits containing commercially valuable hydrocarbons or minerals. The generated models and predicted locations can be used in a well system operation plan. A computer program product is also disclosed that can retrieve sets of known paleogeographic data and generate multiple interim models and parameters that can be used for further predictions on where, and at what depth, valuable deposits may be found. Additionally, a basin fill modeling system is disclosed that can retrieve and store known characteristic parameters for various geographic locations and time periods and utilize those characteristic parameters in algorithms to generate basin fill models and to predict where valuable submarine fan deposits are located.
Embodiments of the subject technology provide for receiving real-time drilling data comprising different drilling parameters measured during a drilling operation. The subject technology calculates a kick detection parameter based at least in part on the different drilling parameters. The subject technology detects an occurrence of a kick during the drilling operation when the kick detection parameter deviates from a trend formed by previously calculated kick detection parameters. Further, the subject technology activates an alarm during the drilling operation in response to detected occurrence of the kick to facilitate preventing a blowout.
A system and method for controlling a drilling tool inside a wellbore makes use of simulated annealing and Bayesian optimization to determine optimum controllable drilling parameters. In some aspects, a computing device generates sampled exploration points using simulated annealing and runs a Bayesian optimization using a loss function and the exploration points to optimize at least one controllable drilling parameter to achieve a predicted value for a selected drilling parameter. In some examples, the selected drilling parameter is rate-of-penetration (ROP) and in some examples, the controllable drilling parameters include such parameters as rotational speed (RPM) and weight-on-bit (WOB). In some examples, the computing device applies the controllable drilling parameter(s) to the drilling tool to achieve the predicted value for the selected drilling parameter and provide real-time, closed-loop control and automation in drilling.
E21B 44/00 - Automatic control systems specially adapted for drilling operations, i.e. self-operating systems which function to carry out or modify a drilling operation without intervention of a human operator, e.g. computer-controlled drilling systems; Systems specially adapted for monitoring a plurality of drilling variables or conditions
Analysis and display of source-to-sink information according to some aspects includes grouping geochronological data associated with a sediment sample into optimized subpopulations within a reference population and target populations, and producing Gaussian functions for the reference population and the target populations using the subpopulations as a priori constraints. The Gaussian functions describe a distribution of zircons. The subpopulations within the reference population and the target populations are compared based on at least one statistical attribute from the Gaussian functions to identify areas of sediment provenance, and the areas of sediment provenance are displayed in various ways, for example, on a paleographic map as of an age of deposition of the sediment sample. A sink-to-sink analysis can also be performed to identify dissimilarities between samples.
Analysis and display of source-to-sink information according to some aspects includes grouping target geochronological data and reference geochronological data into distinct population groups representing a reference population and target populations and characterizing subpopulations within the reference population and the target populations according a statistical attribute or statistical attributes. Subpopulations are compared within the reference population and the target populations based on the statistical attribute or attributes to determine correlations between the reference population and the target populations, and the results can be displayed in many different ways. As one example, results can be displayed using a present day geographic map as well as using a geodynamic plate tectonic model to show data points and their paleogeographic locations for the relevant geological time frame of investigation.
Systems and methods of the present disclosure are directed to reservoir simulation modeling using upon rock compaction tables derived from physical pore compressibility tests. The illustrative methods transform rock mechanics-based pore compressibility tests into compliant rock compaction tables for reservoir simulators using Dimensionless Stress to Pore Pressure Conversion, to thereby transfer geomechanical changes due to confining stress into expressions of geomechanical changes due to pore pressure.
A method and system for automating a reservoir simulation. The method includes identifying a simulation parameter associated with a simulation resource to perform a computer-based reservoir simulation using reservoir data associated with a subterranean reservoir and configuring the simulation resource using a simulation engine to include the simulation parameter for performing the reservoir simulation with a reduced likelihood of simulation failure. The method also includes performing the reservoir simulation using the configured simulation resource and the reservoir data to generate reservoir simulation data and evaluate the reservoir.
An abnormal trend detection system for detecting one or more hazards may provide for a safe and effective drilling operation as any of the one or more hazards may be avoided. Several indicators may be defined including a first, second, third and fourth indicator. The indicators are used to identify in a trend analysis abnormal trends. One or more thresholds may be defined. When a trend analysis indicates that a threshold has been reached or exceeded an alarm may be triggered, a drilling operation may be altered or a combination thereof.
E21B 49/00 - Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells
63.
OPERATING WELLBORE EQUIPMENT USING A DATA DRIVEN PHYSICS-BASED MODEL
Aspects of the present disclosure relate to receiving data associated with a subterranean reservoir to be penetrated by a wellbore and training a neural network with both the data and a physics-based first principles model. The neural network is then used to make predictions regarding the properties of the subterranean reservoir, and these predictions are in turn used to determine one or more controllable parameters for equipment associated with a wellbore. The controllable parameters can then be used to control equipment for formation, stimulation, or production relative to the wellbore.
Fluid production can be simulated using a reservoir model and a tubing model. For example, pressure data and saturation data can be received from a reservoir model simulating a hydrocarbon reservoir in a subterranean formation. A tubing model can be generated by performing nodal analysis using the pressure data and the saturation data. A well-test result can be received that indicates an amount of fluid produced by the wellbore at a particular time. A tuned tubing model can be generated by adjusting the tubing model such that a tubing-model estimate of the amount of fluid produced by the wellbore at the particular time matches the well-test result. An estimated amount of fluid produced by the wellbore can then be determined using the tuned tubing model. The estimated amount of fluid produced by the wellbore may be used for production allocation or controlling a well tool.
A system for multi-stage placement of material in a wellbore includes a recurrent neural network that can be configured based on data from a multi- stage, stimulated wellbore. A computing device in communication with a sensor and a pump is operable to implement the recurrent neural network, which may include a long short-term neural network model (LSTM). Surface data from the sensor at each observation time of a plurality of observation times is used by the recurrent neural network to produce a predicted value for a response variable at a future time, and the predicted value for the response variable is used to control a pump being used to place the material.
A method and a system for modeling a three-dimensional geological structure. A method may comprise selecting input data from well measurement systems, seismic surveys or other sources, inputting the input data into an information handling system, building a quotient space, projecting constraints to the quotient space, constructing depth functions on the quotient space, trimming against a fault network, and producing a three-dimensional model of horizons. A system may comprise a downhole tool. The downhole tool may comprise at least one receiver and at least one transmitter. The system may further comprise a conveyance and an information handling system. The information handling system may be configured to select an input data, build a quotient space, project constraints to the quotient space, construct depth functions on the quotient space, trim against a fault network, and produce a three-dimensional model of a geological structure.
System and methods for optimizing parameters for drilling operations are provided. Real-time data including values for input variables associated with a current stage of a drilling operation along a planned well path are acquired. A neural network model is trained to produce an objective function defining a response value for at least one operating variable of the drilling operation. The response value for the operating variable is estimated based on the objective function produced by the trained neural network model. Stochastic optimization is applied to the estimated response value so as to produce an optimized response value for the operating variable. Values of controllable parameters are estimated for a subsequent stage of the drilling operation, based on the optimized response value of the operating variable. The subsequent stage of the drilling operation is performed based on the estimated values of the controllable parameters.
E21B 44/00 - Automatic control systems specially adapted for drilling operations, i.e. self-operating systems which function to carry out or modify a drilling operation without intervention of a human operator, e.g. computer-controlled drilling systems; Systems specially adapted for monitoring a plurality of drilling variables or conditions
E21B 41/00 - Equipment or details not covered by groups
A method of managing oilfield activity with a control system is provided having a plurality of virtual sensors and integrating the virtual sensors into a virtual sensor network. The method includes determining interdependencies among the virtual sensors, obtaining operational information from the virtual sensors, and providing virtual sensor output to the control system based on the determined interdependencies and the operational information.
A system for real-time steering of a drill bit includes a drilling arrangement and a computing device in communication with the drilling arrangement. The system iteratively, or repeatedly, receives new data associated with the wellbore. At each iteration, a model, for example an engineering model, is applied to the new data to produce an objective function defining the selected drilling parameter. The objective function is modified at each iteration to provide an updated value for the selected drilling parameter and an updated value for at least one controllable parameter. In one example, the function is modified using Bayesian optimization. The system iteratively steers the drill bit to obtain the updated value for the selected drilling parameter by applying the updated value for at least one controllable parameter over the period of time that the wellbore is being formed.
E21B 44/00 - Automatic control systems specially adapted for drilling operations, i.e. self-operating systems which function to carry out or modify a drilling operation without intervention of a human operator, e.g. computer-controlled drilling systems; Systems specially adapted for monitoring a plurality of drilling variables or conditions
A system and method for controlling a drilling tool inside a wellbore makes use of projection of optimal rate of penetration (ROP) and optimal controllable parameters such as weight-on-bit (WOB), and rotations-per-minute (RPM) for drilling operations. Optimum controllable parameters for drilling optimization can be predicted using a data generation model to produced synthesized data based on model physics, an ROP model, and stochastic optimization. The synthetic data can be combined with real-time data to extrapolate the data across the WOB and RPM space. The values for WOB an RPM can be controlled to steer a drilling tool. Examples of models used include a non-linear model, a linear model, a recurrent generative adversarial network (RGAN) model, and a deep neural network model.
E21B 44/00 - Automatic control systems specially adapted for drilling operations, i.e. self-operating systems which function to carry out or modify a drilling operation without intervention of a human operator, e.g. computer-controlled drilling systems; Systems specially adapted for monitoring a plurality of drilling variables or conditions
E21B 41/00 - Equipment or details not covered by groups
71.
METHOD AND SYSTEM FOR ANALYZING A DRILL STRING STUCK PIPE EVENT
A method includes receiving a plurality of drilling parameters from a drilling operation, wherein the plurality of drilling parameters. The drilling parameters include a cuttings bed height and a friction factor between a drill string and a wellbore. The method further includes applying the plurality of drilling parameters to a friction model. The friction model utilizes a function of the cuttings bed height to determine a comprehensive friction factor. The comprehensive friction factor is applied to the plurality of drilling parameters to determine a required torque or hook load of the drill string. The method further includes providing an indication of a stuck pipe event.
E21B 44/00 - Automatic control systems specially adapted for drilling operations, i.e. self-operating systems which function to carry out or modify a drilling operation without intervention of a human operator, e.g. computer-controlled drilling systems; Systems specially adapted for monitoring a plurality of drilling variables or conditions
E21B 41/00 - Equipment or details not covered by groups
72.
FAULT DETECTION BASED ON SEISMIC DATA INTERPRETATION
A method for determining a position of a geological feature in a formation includes acquiring a seismic dataset, wherein the seismic dataset is based on signals of one or more seismic sensors and determining a set of indicators of candidate discontinuities in the formation based on the seismic dataset. The method also includes labeling a subset of the set of indicators of candidate discontinuities using a neural network with a label based on the set of indicators of candidate discontinuities, wherein the label distinguishes an indicator of a candidate discontinuity between being an indicator of a target discontinuity or being an indicator of a non-target discontinuity and determining the position of the geological feature in the formation, wherein the geological feature in the formation is associated with at least one target discontinuity based on the subset of the set of indicators of candidate discontinuities.
A method includes receiving a training selection of a first set of faults located in a first subset of a seismic dataset for a subsurface geologic formation, detecting a second set of faults in the seismic dataset based on fault interpretation operations using a first set of interpretation parameters, and determining a difference between the first set of faults and the second set of faults. The method also includes generating a second set of interpretation parameters for the fault interpretation operations based on the difference between the first set of faults and the second set of faults, and determining a feature of the subsurface geologic formation based on fault interpretation operations using the second set of interpretation parameters.
Embodiments of the subject technology for deep learning based reservoir modelling provides for receiving input data comprising information associated with one or more well logs in a region of interest. The subject technology determines, based at least in part on the input data, an input feature associated with a first deep neural network (DNN) for predicting a value of a property at a location within the region of interest. Further, the subject technology trains, using the input data and based at least in part on the input feature, the first DNN. The subject technology predicts, using the first DNN, the value of the property at the location in the region of interest. The subject technology utilizes a second DNN that classifies facies based on the predicted property in the region of interest.
A method may include drilling a deviated wellbore penetrating a subterranean formation according to bottom hole assembly parameters and surface parameters; collecting real-time formation data during drilling; updating a model of the subterranean formation based on the real-time formation data and deriving formation properties therefrom; collecting survey data corresponding to a location of a drill bit in the subterranean formation; deriving a target well path for the drilling based on the model of the subterranean formation; deriving a series of trajectory well paths based on the formation properties, the survey data, the bottom hole assembly parameters, and the surface parameters and uncertainties associated therewith; deriving an actual well path based on the series of trajectory well paths; deriving a deviation between the target well path and the actual well path; and adjusting the bottom hole assembly parameters and the surface parameters to maintain the deviation below a threshold.
E21B 44/00 - Automatic control systems specially adapted for drilling operations, i.e. self-operating systems which function to carry out or modify a drilling operation without intervention of a human operator, e.g. computer-controlled drilling systems; Systems specially adapted for monitoring a plurality of drilling variables or conditions
E21B 49/00 - Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells
76.
MAPPING VERTICES FROM AN EARTH MODEL TO A 2D ARRAY
A system and method for mapping vertices from an earth model to a 2D array, comprising: (a) aligning the earth model and the 2D array (b) processing each vertex on a respective vertical curve of the earth model and the respective vertical curve (c) processing each next unprocessed vertex on the respective vertical curve (d) forming at least one current curve (e) processing each unprocessed vertex between each respective current curve and reference line in the 2D array (f) optimizing a spacing between the vertices marked in each another respective horizontal line in the 2D array (g) repeating steps (c) through (f) using a computer processor until there are no more unprocessed vertices.
Systems, methods, and computer-readable media are described for the mutual improvement of physics-based and data-driven models related to an oilfield. These may involve generating, via a processor, with an oilfield related condition as a first input, a first output based on one of a physics-based model or a data-based model; generating, using the first input or a second input, a second output based on the other of the physics-based model or the data-based model not used to generate the first output; and modifying, automatically, at least one of the physics-based model, data-driven model, the first input or the second input, based on the first output or second output.
Systems, methods, and computer-readable media are described for intelligent, real-time monitoring and managing of changes in oilfield equilibrium to optimize production of desired hydrocarbons and economic viability of the field. In some examples, a method can involve generating, based on a topology of a field of wells, a respective graph for the wells, each respective graph including computing devices coupled with one or more sensors and/or actuators. The method can involve collecting, via the computing devices, respective parameters associated with one or more computing devices, sensors, actuators, and/or models, and identifying a measured state associated with the computing devices, sensors, actuators, and/or models. Further, the method can involve automatically generating, based on the respective graph and respective parameters, a decision tree for the measured state, and determining, based on the decision tree, an automated adjustment for modifying production of hydrocarbons and/or an economic parameter of the hydrocarbon production.
Systems and methods for determining vector-ratio safety factors for wellbore tubular design are provided. Pressure and temperature data for at least one load point along a tubular component of a wellbore are obtained. An effective failure axial load expected at the load point is calculated during a downhole operation to be performed along one or more sections of the wellbore within a subsurface formation, based on the obtained data. An upper boundary and a lower boundary for the effective failure axial load are determined, based on physical properties of the tubular component at the load point. A midpoint of the effective failure axial load is calculated based on the upper and lower boundaries. A critical failure differential pressure is calculated, based on the midpoint of the effective failure axial load. A vector-ratio safety factor is calculated, based on the critical failure differential pressure relative to the effective failure axial load.
A method includes comparing a natural frequency of a tubular with a frequency of each of at least two pulse generating devices positioned adjacent each other on the tubular, the tubular and the at least two pulse generating devices comprising an energy system; adjusting the frequency of at least one of the at least two pulse generating devices when the frequency is equal to the natural frequency of the tubular and obtaining an adjusted frequency different from the natural frequency; calculating an energy distribution in the energy system based on the natural frequency and the adjusted frequency of the at least one of the at least two pulse generating devices; and determining a new location on the tubular for positioning one or more of the at least two pulse generating devices such that energy introduced into the energy system is less than energy dissipated from the energy system.
Systems and methods to correct misties across multiple 2D seismic surveys using a correction solution calculated based only on the intersecting points between different surveys.
G01V 1/36 - Effecting static or dynamic corrections on records, e.g. correcting spread; Correlating seismic signals; Eliminating effects of unwanted energy
Systems and methods for interpreting and visualizing multi-Z horizons from seismic data are disclosed. A two-dimensional (2D) representation of seismic data is displayed via a graphical user interface (GUI). User input is received via the GUI for interpreting a multi-Z horizon within a portion of the displayed 2D representation. The user's input is tracked relative to displayed 2D representation within the GUI. Based on the tracking, each of a plurality of surfaces for the multi-Z horizon is determined. At least one intersection point between the multi-Z horizon surfaces is identified. A depth position for each surface relative to other surfaces is determined. The 2D representation of the seismic data is dynamically updated to include visual indications for the plurality of surfaces and the intersection point(s), based on the depth position of each surface, where the visual indications use different visualization styles to represent the surfaces and intersection point(s).
Systems and methods of the present disclosure are directed to adjustment of seismic survey boundaries to remove or minimize data gaps, thereby providing optimized seismic interpretation.
Systems and methods for interpreting multi-Z horizons from seismic data are disclosed. Seismic data is displayed via a graphical user interface (GUI) of an application executable at a user's computing device. User input is received via the GUI for picking surfaces of a multi-Z horizon within a current view of the displayed data. The user's input is tracked as it is received via the GUI over a series of input points within the current view of the displayed seismic data. Based on the tracking, each of a plurality of surfaces for the multi-Z horizon and at least one edge point between the picked surfaces within the current view of the seismic data are determined. The current view of the seismic data within the GUI is dynamically updated to include a visual indication of the plurality of surfaces and the at least one edge point for the multi-Z horizon.
Systems and methods for editing multi-Z horizons interpreted from seismic data are provided. A multi-Z horizon having a plurality of surfaces is visualized within a two-dimensional (2D) representation of seismic data displayed via a graphical user interface (GUI) of an application executable at a computing device of a user. Input is received via the GUI from the user for editing one or more of the plurality of surfaces of the multi-Z horizon within a current view of the displayed 2D representation of the seismic data. A location of the received input relative to each of the plurality of surfaces within the current view is determined. The one or more surfaces of the multi-Z horizon are modified based on the location of the received input within the current view. The visualization of the multi-Z horizon within the GUI is updated, based on the modified one or more surfaces.
Systems and methods for automatically tracking multi-Z horizons within seismic volumes are provided. Seed data for each of a plurality of surfaces of a multi-Z horizon within a seismic volume are obtained. A data hull for each surface is generated based on the obtained seed data. A tracking region within the seismic volume is determined, based on the generated data hull. Each surface of the multi-Z horizon is automatically tracked through the tracking region. Upon determining that one or more of the plurality of surfaces violate at least one geological boundary rule associated with the plurality of surfaces, truncating the one or more surfaces such that each surface of the multi-Z horizon honors the geological boundary rule within the seismic volume.
G01V 3/26 - Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination or deviation specially adapted for well-logging operating with magnetic or electric fields produced or modified either by the surrounding earth formation or by the detecting device
G01V 3/38 - Processing data, e.g. for analysis, for interpretation or for correction
87.
DIFFUSION FLUX INCLUSION FOR A RESERVOIR SIMULATION FOR HYDROCARBON RECOVERY
A method includes selecting a model for a simulation of hydrocarbon recovery from a reservoir having a plurality of fractures during injection of an injected gas into the plurality of fractures. Selecting the model includes determining a flux ratio of a convection rate to a diffusion rate for the reservoir, determining whether the flux ratio is less than a threshold, and in response to the flux ratio being less than the threshold, selecting the model that includes diffusion. Selecting the model includes performing the simulation of the hydrocarbon recovery from the reservoir based on the model.
A method includes creating a diffusion model for a simulation of hydrocarbon recovery from a reservoir having a plurality of fractures during injection of an injected gas into the plurality of fractures, wherein the reservoir is partitioned into a plurality of grid cells in the diffusion model. Creating the diffusion model for a grid cell of the plurality of grid cells comprises, determining a flux ratio of a convective flux to an estimated diffusive flux for the grid cell, determining whether the flux ratio is less than a threshold, and in response to determining that the flux ratio is less than the threshold, determining a full diffusive flux for the grid cell for inclusion in the diffusion model. The method also includes performing the simulation of the hydrocarbon recovery from the reservoir based on the diffusion model.
A method of managing a network of wells and surface facilities includes partitioning the network to obtain a pipe sub-network and two or more well sub-networks, and constructing an associated set of equations that represent a steady-state fluid flow in the sub-network. The method further includes setting boundary conditions for each well sub-network, and determining a steady-state flow solution for each well sub-network. The method further includes establishing boundary conditions for the pipe sub-network, and finding a steady-state flow solution for the pipe sub-network. If the solution does not match the estimated pressure, the method further includes adjusting the estimated pressure, repeating said setting, determining, establishing, finding, and adjusting operations until the calculated and estimated pressures converge, and analyzing flow rates of the steady-state flow solutions to evaluate suitability of a modification to the network.
A method for determining active constraint equations in a network of wells and surface facilities includes constructing at least one constraint equation for a connection in the network. Each constraint equation includes a respective slack variable and a respective slack variable multiplier. The method further includes constructing a base equation for the connection. The base equation includes the respective slack variable and another respective slack variable multiplier. The method further includes introducing a pseudo slack variable for another connection in the network such that a Schur complement, of a matrix of constraint and base equations dependent only on slack variable multipliers, is sparse. The method further includes solving for each respective slack variable using the Schur complement matrix. The method further includes adjusting a variable parameter of the network using results from solving for each respective slack variable.
An apparatus and a method for estimating interval anellipticity parameter by inversing effective anellipticity parameter in the depth domain using a least-squares method. One embodiment of interval anellipticity parameter estimator includes: 1) an interface configured to receive seismic data and borehole information; 2) a depth convertor configured to obtain a function of depth of effective anisotropy parameter based on said borehole information; 3) an inverse transformer configured to set up said function of depth of effective anisotropy parameter as a least-squares fitting problem based on said P-wave data; and 4) an iterative solver configured to use iterative methods to solve said least-squares fitting problem and to obtain an anisotropy model containing interval anellipticity parameter.
A system provides thermal and stress analysis of complex well operations above the end of the downhole string to meet the analysis needs of downhole operations such as hydraulic fracturing in unconventional oil and gas field development.
A multivariate analysis may be used to correlate seismic attributes for a subterranean formation with petrophysical properties of the subterranean formation and/or microseismic data associated with treating, creating, and/or extending a fracture network of the subterranean formation. For example, a method may involve modeling petrophysical properties of a subterranean formation, microseismic data associated with treating a complex fracture network in the subterranean formation, or a combination thereof with a mathematical model based on measured data, microseismic data, completion and treatment data, or a combination thereof to produce a petrophysical property map, a microseismic data map, or a combination thereof; and correlating a seismic attribute map with the petrophysical property map, the microseismic data map, or the combination thereof using the mathematical model to produce at least one quantified correlation, wherein the seismic attribute map is a seismic attributed modeled for the complex fracture network.
A method may comprise: modeling a complex fracture network within the subterranean formation with a mathematical model based on a natural fracture network map and measured data of the subterranean formation collected in association with a fracturing treatment of the subterranean formation to produce a complex fracture network map; importing microseismic data collected in association with the fracturing treatment of the subterranean formation into the mathematical model; identifying directions of continuity in the microseismic data via a geostatistical analysis that is part of the mathematical model; and correlating the directions of continuity in the microseismic data to the complex fracture network with the mathematical model to produce a microseismic-weighted (MSW) complex fracture network map.
Systems and methods for modeling petroleum reservoir properties using a gridless reservoir simulation model are provided. Data relating to geological properties of a reservoir formation is analyzed. A tiered hierarchy of geological elements within the reservoir formation is generated at different geological scales, based on the analysis. The geological elements at each of the different geological scales in the tiered hierarchy are categorized. Spatial boundaries between the categorized geological elements are defined for each of the geological scales in the tiered hierarchy. A scalable and updateable gridless model of the reservoir formation is generated, based on the spatial boundaries defined for at least one of the geological scales in the tiered hierarchy.
This disclosure presents a new tool and methodology to work, in a single-trip system, and create conduits through the casing or tubing to access the annuli, circulate out the annuli contents, accurately deliver the cement required to create the subsequent barrier. The tool and system provided by this disclosure provide a cost effective way of plugging an oil and gas well in a single trip without the need to remove in-place production casing.
Systems and methods for determining a numerical age for new geological events within a new scheme by ordering relations between geological events within a new scheme and/or within a new scheme and a preexisting scheme into a preferred hierarchy, dynamically excluding lower relations in the preferred hierarchy that conflict with higher relations due to irreconcilable ages of the relations, and using the ordered relations remaining in the preferred hierarchy to determine a numerical age for the new geological events within the new scheme.
A method includes obtaining, using multiple internal well -modeling applications, data from an engineer's data model (EDM) database. The method further includes deriving data relevant to an external application from at least a first portion of the obtained data. The method further includes formatting the derived data and at least a second portion of the obtained data, relevant to the external application, to be compatible with the external application. The method further includes sending the formatted data to the external application or a user of the external application. The method further includes receiving results of processing the formatted data by the external application or the user of the external application. The method further includes storing the results in the EDM database.
Various embodiments include systems and methods of operating the systems that include operation of a plurality of first nodes and second nodes in response to a request, where each first node is a first type of processing unit and each second node is a second type of processing unit, where the second type of processing node is different from the first type of processing node. Each of the first and second nodes can be operable in parallel with the other nodes of their respective plurality. Each second node may be operable to respond to the request using data and/or metadata it holds and/or operable in response to data and/or metadata from one or more of the first nodes. Additional apparatus, systems, and methods are disclosed.
G06F 9/38 - Concurrent instruction execution, e.g. pipeline, look ahead
H04N 21/2343 - Processing of video elementary streams, e.g. splicing of video streams or manipulating MPEG-4 scene graphs involving reformatting operations of video signals for distribution or compliance with end-user requests or end-user device requirements
G06F 15/16 - Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs
G06T 1/20 - Processor architectures; Processor configuration, e.g. pipelining
A method includes generating a faulted point cloud representing a faulted geological formation including a first fault block having a first surface, a second fault block having a second surface, and a fault formed therebetween and having a fault surface, generating a first fault trace from an intersection of the first surface and the fault surface and a second fault trace from an intersection of the second surface and the fault surface, generating a first fault polygon using the first and second fault traces, generating a center polyline, generating third and fourth fault traces separated from the first and second fault traces, respectively, generating a second fault polygon using the third and fourth fault traces, expanding a first area including the first and third fault traces, expanding a second area including the second and fourth fault traces, and generating an unfaulted point cloud representing an unfaulted geological formation.
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