公开(公告)号：US20230141334A1

公开(公告)日：2023-05-11

申请号：US17982839

申请日：2022-11-08

Applicant: ConocoPhillips Company

Inventor： Alexander J. Wagner ,  Christopher S. Olse S. Olsen ,  Tahmineh Nazari ,  Megan Potter ,  Thiago B. Simoes Correa ,  Daniel P. Sheehan ,  Brackin A. Smith ,  Douglas S. Moore ,  Randy E. John ,  Zachary A. Wallace

IPC: G06N5/02

CPC classification number: G06N5/022

Abstract: Systems and methods include a geological structure modeling tool for generating a geological facies model for a target well with decision tree-based models. The decision tree-based models use geographic facie class as a target variable and receives an input data set including well log data, core data, and geological facie class labels (e.g., generated by a subject matter expert (SME)). A predictive analytics model using the decision tree-based models generates, based on an input of target well data, the geological facies model to represent underlying geological structures at a candidate location (e.g., for drilling a well) or a section of a subsurface reservoir (e.g., for resource characterization). Vertical context data can be provided to the decision tree-based models and the input data set can be artificially boosted based on geological facies class label occurrences. A well development action is selected for the candidate location based on the geological facies model.

公开(公告)号：US20230142230A1

公开(公告)日：2023-05-11

申请号：US17982878

申请日：2022-11-08

Applicant: ConocoPhillips Company

Inventor： Amir Nejad ,  Christopher S. Olsen ,  Bo Hu ,  Xin Luo ,  Qing Chen ,  Alexander J. Wagner ,  Liu Chao Zhang ,  Iman Shahim ,  Curt E. Schneider ,  David D. Smith ,  Andy Flowers ,  Richard Barclay

IPC: E21B43/16

CPC classification number: E21B43/16

Abstract: Implementations described and claimed herein provide systems and methods for dynamic waterflood forecast modeling utilizing deep thinking computational techniques to reduce the processing time for generating the forecast model and improving the accuracy of resulting forecasts. In one particular implementation, a dataset of a field may be restructured into the spatio-temporal framework and data driven deep neural networks may be utilized to learn the nuances of data interactions to make more accurate forecasts for each well in the field. Further, the generated model may forecast a single time segment and build the complete forecast through recursive prediction instances. The temporal component of the restructured data may include all or a portion of the production history of the field divided into spaced time intervals. The spatial component of the restructure data may include, within each epoch, a computed or estimated spatial relationships of all existing wells.

公开(公告)号：US20230142526A1

公开(公告)日：2023-05-11

申请号：US17982926

申请日：2022-11-08

Applicant: ConocoPhillips Company

Inventor： Qing Chen ,  Xin Luo ,  Amir Nejad ,  Bo Hu ,  Christopher S. Olsen ,  Alexander J. Wagner ,  Iman Shahim ,  Curt E. Schneider ,  David D. Smith ,  Andy Flowers ,  Liu Chao Zhang

IPC: G06F30/28

CPC classification number: G06F30/28

Abstract: Systems and method for predicting production decline for a target well include generating a static model and a decline model to generate a well production profile. The static model is generated with supervised machine learning using an input data set including historical production data, and calculates an initial resource production rate for the target well. The decline model is generated with a neural network using the input data and dynamic data (e.g., an input time interval and pressure data of the target well), and calculates a plurality of resource production rates for a plurality of time intervals. The system can perform multiple recursive calculations to calculate the plurality of resource production rates, generating the well production profile. For instance, the predicted resource production rate of a first time interval is used as one of inputs for predicting the resource production rate for a second, subsequent time interval.