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.