公开(公告)号：US11428086B2

公开(公告)日：2022-08-30

申请号：US17187479

申请日：2021-02-26

Applicant: CONOCOPHILLIPS COMPANY

Inventor： Qing Chen ,  Wendell P. Menard

IPC: E21B43/14 ,  E21B43/24 ,  E21B43/30

Abstract: Single well SAGD is improved by having one or more injection segments and two or more production segments between the toe end and the heel end of a flat, horizontal well. The additional injection points improve the rate of steam chamber development as well as the rate of production, as shown by simulations of a central injection segment bracketed by a pair of production segments (-P-I-P-), and by a pair of injection segments with three production segments (-P-I-P-I-P). Although the completion of the single well costs more, this configuration allows the development of thin plays that cannot be economically developed with traditional SAGD wellpairs.

公开(公告)号：US11668176B2

公开(公告)日：2023-06-06

申请号：US17481530

申请日：2021-09-22

Applicant: CONOCOPHILLIPS COMPANY

Inventor： Bo Chen ,  Qing Chen

IPC: E21B43/30 ,  E21B43/24 ,  E21B43/16

CPC classification number: E21B43/305 ,  E21B43/2408 ,  E21B43/164 ,  E21B43/168

Abstract: A well configuration for co-injection processes, wherein a horizontal producer well at the bottom of the pay is combined with injection or injection and producer wells that are vertical and above the lower horizontal production well. This well arrangement minimizes “blanket” effects by non-condensable gases.

公开(公告)号：US10995596B2

公开(公告)日：2021-05-04

申请号：US15363403

申请日：2016-11-29

Applicant: ConocoPhillips Company

Inventor： Qing Chen ,  Wendell P. Menard

IPC: E21B43/14 ,  E21B33/12 ,  E21B43/24 ,  E21B43/30

Abstract: The present disclosure relates to a particularly effective well configuration that can be used for single well cross steam assisted gravity drainage (SW-XSAGD) wherein a single well has multiple injection sections each separated by a production segment that is completed with passive FCDs to control steam flashing.

公开(公告)号：US20230237225A1

公开(公告)日：2023-07-27

申请号：US18100928

申请日：2023-01-24

Applicant: ConocoPhillips Company

Inventor： Chung-Kan Huang ,  Qing Chen

IPC: G06F30/27

CPC classification number: G06F30/27

Abstract: Systems and methods for reservoir modeling use reservoir simulation and production data to predict future production for one or more wells. The system receives static data of a reservoir or well, receives dynamic data of the reservoir or well, and processes the static data and the dynamic data to generate a reservoir model. For instance, the static data and dynamic data can be used to generate a Voronoi grid, which is used to create a spatio-temporal dataset representing time steps for a focal well and offset wells. The reservoir model can predict reservoir performance, field development, production metrics, and operation metrics. By using one or more Machine Learning (ML) models, the systems disclosed herein can determined reservoir physics in minutes and replicate the physical properties calculated by more complex and computationally intensive reservoir modeling.

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.

公开(公告)号：US20230140905A1

公开(公告)日：2023-05-11

申请号：US17982799

申请日：2022-11-08

Applicant: ConocoPhillips Company

Inventor： Bo Hu ,  Qing Chen ,  Amir Nejad ,  Xin Luo ,  Christopher S. Olsen ,  Robert C. Burton ,  Liang Zhou ,  Xin Jun Gou ,  Liu Chao Zhang ,  Junjing Zhang ,  Iman Shahim ,  Curt E. Schneider ,  David D. Smith ,  Andy Flowers

IPC: G06F30/28 ,  G06N3/08

CPC classification number: G06F30/28 ,  G06N3/08

Abstract: Implementations described and claimed herein provide systems and methods for a framework to achieve completion optimization for waterflood field reservoirs. The proposed methodology leverages adequate data collection, preprocessing, subject matter expert knowledge-based feature engineering for geological, reservoir and completion inputs, and state-of-the-art machine-learning technologies, to indicate important production drivers, provide sensitivity analysis to quantify the impacts of the completion features, and ultimately achieve completion optimization. In this analytical framework, model-less feature ranking based on mutual information concept and model-dependent sensitivity analyses, in which a variety of machine-learning models are trained and validated, provides comprehensive multi-variant analyses that empower subject-matter experts to make a smarter decision in a timely manner.

公开(公告)号：US11156072B2

公开(公告)日：2021-10-26

申请号：US15673809

申请日：2017-08-10

Applicant: ConocoPhillips Company

Inventor： Bo Chen ,  Qing Chen

IPC: E21B43/30 ,  E21B43/24 ,  E21B43/16

Abstract: A well configuration for co-injection processes, wherein a horizontal producer well at the bottom of the pay is combined with injection or injection and producer wells that are vertical and above the lower horizontal production well. This well arrangement minimizes “blanket” effects by non-condensable gases.

公开(公告)号：US10526881B2

公开(公告)日：2020-01-07

申请号：US14955894

申请日：2015-12-01

Applicant: CONOCOPHILLIPS COMPANY

Inventor： Bo Chen ,  Qing Chen

IPC: E21B43/24

Abstract: Producing hydrocarbons by steam assisted gravity drainage, more particularly, utilizing conventional horizontal wellpair configuration of SAGD in conjunction of infill production well, to coinject oil-based solvents with steam initially and then switch to NCG-steam coinjection after establishing thermal communication between the thermal chamber and infill well.

公开(公告)号：US10287864B2

公开(公告)日：2019-05-14

申请号：US14956030

申请日：2015-12-01

Applicant: CONOCOPHILLIPS COMPANY

Inventor： Bo Chen ,  Qing Chen ,  Thomas J. Wheeler

IPC: E21B43/16 ,  E21B43/24 ,  E21B43/30

Abstract: Producing hydrocarbons by steam assisted gravity drainage, more particularly utilizing conventional horizontal wellpair configuration of SAGD in conjunction of infill production wells the production wells comprising two or more fishbone lateral wells to inject steam initially and then switch to NCG-steam coinjection after establishing thermal communication between the thermal chamber and infill well.

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.