公开(公告)号：US11713675B2

公开(公告)日：2023-08-01

申请号：US16865063

申请日：2020-05-01

Applicant: Landmark Graphics Corporation

Inventor： Michael Charles Quintrell Treloar ,  Owen Edward Sutcliffe

IPC: E21B49/00 ,  G01V99/00 ,  G06T11/60 ,  G06T11/20

CPC classification number: E21B49/00 ,  G01V99/005 ,  G06T11/203 ,  G06T11/60

Abstract: 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.

公开(公告)号：US11703608B2

公开(公告)日：2023-07-18

申请号：US17136838

申请日：2020-12-29

Applicant: Landmark Graphics Corporation

Inventor： Kalyan Saikia ,  Samiran Roy

IPC: G01V1/30 ,  G06N5/04 ,  G06N20/00

CPC classification number: G01V1/307 ,  G06N5/04 ,  G06N20/00 ,  G01V2210/63

Abstract: A system can determine a location for future wells using machine-learning techniques. The system can receive seismic data about a subterranean formation and may determine a set of seismic attributes from the seismic data. The system can block the set of seismic attributes into a set of blocked seismic attributes by distributing the set of seismic attributes onto a geo-cellular grid representative of the subterranean formation. The system can apply a trained machine-learning model to the set of blocked seismic attributes to generate a composite seismic parameter. The system can distribute the composite seismic parameter in the subterranean formation to characterize formation locations based on a predicted presence of hydrocarbons.

公开(公告)号：US11697989B2

公开(公告)日：2023-07-11

申请号：US17004175

申请日：2020-08-27

Applicant: Landmark Graphics Corporation

Inventor： Manish K. Mittal ,  Robello Samuel

IPC: E21B44/00 ,  E21B44/08 ,  E21B47/003 ,  E21B49/00 ,  E21B45/00 ,  G06Q10/0639

CPC classification number: E21B44/08 ,  E21B44/00 ,  E21B45/00 ,  E21B47/003 ,  E21B49/003 ,  G06Q10/06393

Abstract: A system is described for calculating and outputting micro invisible lost time (MILT). The system may include a processor and a non-transitory computer-readable medium comprising instructions that are executable by the processor to cause the processor to perform various operations. Time-stamp data that includes values of drilling parameters may be received about a drilling operation, and the values of drilling parameters may be classified into a rig state that includes rig activities. For each rig activity, an actual completion time may be determined and compared to an expected completion time for determining a deviation. At least one deviated activity, in which the deviation is greater than a threshold, may be determined. Deviations may be combined into MILT that can be output for controlling the drilling operation.

公开(公告)号：US20230205948A1

公开(公告)日：2023-06-29

申请号：US17560982

申请日：2021-12-23

Applicant: Landmark Graphics Corporation

Inventor： Yogesh Bansal ,  Gerardo Mijares

IPC: G06F30/27 ,  G06F30/13 ,  E21B47/00 ,  E21B49/00

CPC classification number: G06F30/27 ,  G06F30/13 ,  E21B47/00 ,  E21B49/00 ,  E21B2200/20 ,  E21B2200/22

Abstract: Systems and methods for completion design are disclosed. Wellsite data is acquired for one or more existing production wells. The wellsite data is transformed into model data sets for training a first machine learning (ML) model to predict well logs. A first well model uses the well logs to estimate production of the existing well(s). Parameters of the first well model are tuned based on a comparison between the estimated and actual production of the existing well(s). A second ML model is trained to predict parameters of a second well model for a new well, based on the tuned parameters of the first well model. The new well's production is forecasted using the second ML model. Completion costs for the new well are estimated based on the well's completion design parameters and the forecasted production. Completion design parameters are adjusted, based on the estimated completion costs and the forecasted production.

公开(公告)号：US20230193754A1

公开(公告)日：2023-06-22

申请号：US17556092

申请日：2021-12-20

Applicant: Landmark Graphics Corporation

Inventor： Yogesh Bansal ,  Gerardo Mijares

IPC: E21B49/08 ,  E21B47/12

CPC classification number: E21B49/087 ,  E21B47/138 ,  E21B2200/22

Abstract: Systems and methods for machine learning (ML) assisted parameter matching are disclosed. Wellsite data is acquired for one or more existing production wells in a hydrocarbon producing field. The wellsite data is transformed into one or more model data sets for predictive modeling. A first ML model is trained to predict well logs for the existing production well(s), based on the model data set(s). A first well model is generated to estimate production of the existing production well(s) based on the predicted well logs. Parameters of the first well model are tuned based on a comparison between the estimated and an actual production of the existing production well(s). A second ML model is trained to predict parameters of a second well model for a new production well, based on the tuned parameters of the first well model. The new well’s production is forecasted using the second ML model.

公开(公告)号：US20230193725A1

公开(公告)日：2023-06-22

申请号：US17553219

申请日：2021-12-16

Applicant: Landmark Graphics Corporation

Inventor： Margareth Gibbons Parra ,  Adolfo Gonzales ,  Nitish Damodar Chaudhari ,  Gabriel Tirado

IPC: E21B41/00 ,  G06F30/27 ,  E21B47/12

CPC classification number: E21B41/00 ,  G06F30/27 ,  E21B47/12 ,  E21B2200/22

Abstract: The disclosure presents processes for evaluating a borehole design against one or more identified risks. The processes can determine borehole design concepts for the borehole design. Each borehole design concept can have multiple risks assigned, which can be selected from a library of risks, a risk matrix or template, a risk model, or user entered risks. The risks can be scored using one or more statistics-based algorithms, such as a sum, an average, a mean, or other algorithms. The risks can be grouped by a risk level, forming a sub-risk score for each risk level for each borehole design concept. A final risk score can be generated using the sub-risk scores for the borehole design. More than one borehole design can be evaluated using a risk tolerance parameter and the borehole design that satisfies the risk tolerance parameter can be selected as the recommended borehole design.

公开(公告)号：US11644593B2

公开(公告)日：2023-05-09

申请号：US16682981

申请日：2019-11-13

Applicant: LANDMARK GRAPHICS CORPORATION

Inventor： Chao Gao ,  Travis St. George Ramsay ,  Felix Segovia

IPC: G01V1/30 ,  G01V1/36

CPC classification number: G01V1/308 ,  G01V1/36 ,  G01V2210/512 ,  G01V2210/514

Abstract: 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.

公开(公告)号：US20230095708A1

公开(公告)日：2023-03-30

申请号：US17054629

申请日：2020-03-26

Applicant: Landmark Graphics Corporation

Inventor： Avinash Wesley ,  Robello Samuel ,  Manish K. Mittal

IPC: E21B44/00 ,  E21B47/007

Abstract: 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.

公开(公告)号：US11608732B2

公开(公告)日：2023-03-21

申请号：US17729545

申请日：2022-04-26

Applicant: Landmark Graphics Corporation

Inventor： Robello Samuel ,  Wenjun Huang

IPC: E21B45/00 ,  E21B44/00 ,  E21B44/04

Abstract: Certain aspects and features relate to a system that includes a drilling tool, a processor, and a non-transitory memory device that includes instructions that are executable by the processor to cause the processor to perform operations. The operations include receiving input data that corresponds to characteristics of at least one of drilling fluid, a drillstring, or a wellbore. The operations also include calculating at least one dynamic sideforce and at least one dynamic, hydraulic force based at least in part on the input data. The operations also include determining an equilibrium solution for an output value using the at least one dynamic sideforce and at least one dynamic, hydraulic force. The operations also include applying the output value to the drilling tool for controlling operation of the drilling tool.

公开(公告)号：US11599790B2

公开(公告)日：2023-03-07

申请号：US16614858

申请日：2017-07-21

Applicant: Landmark Graphics Corporation

Inventor： Yogendra Narayan Pandey ,  Keshava Prasad Rangarajan ,  Jeffrey Marc Yarus ,  Naresh Chaudhary ,  Nagaraj Srinivasan ,  James Etienne

IPC: G06N3/08 ,  G01V99/00 ,  G06K9/62

Abstract: 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.