Systems and methods of predictive decline modeling for a well

Patent metadata

Field	Value
Publication number	US-2023142526-A1
Application number	US-202217982926-A
Country	US
Kind code	A1
Filing date	Nov 8, 2022
Priority date	Nov 8, 2021
Publication date	May 11, 2023
Grant date	—

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.

First claim

What is claimed is:

1 . A method for predictive decline modeling for an oil well, the method comprising:
generating a static model based on an input data set including historical production data corresponding to one or more wells; generating a decline model based on the historical production data and dynamic well data; and generating a predicted well production profile for a target well by:
determining, using the static model and one or more well features of the target well, a predicted initial resource production rate for the target well;
determining, using the decline model and the predicted initial resource production rate, a first final resource production rate for the target well at a first time interval; and
determining, using the decline model and the first final resource production rate at the first time interval, a second final resource production rate at a second time interval subsequent to the first time interval.

2 . The method of claim 1 , wherein generating the predicted well production profile includes recursive calculations generating resource production rates for a series of time intervals.

3 . The method of claim 1 , wherein the static model is generated with supervised machine learning using the historical production data as feature inputs and a target variable being an initial resource production rate having a 30 days-averaged Initial Production (IP30) value.

4 . The method of claim 1 , wherein the historical production data represents one or more of a geological feature, well completion parameters, reservoir properties, production data, injection data, and fluid data.

5 . The method of claim 1 , wherein the decline model is generated with a neural network using the historical production data and the dynamic well data as feature inputs and a target variable being resource production rate at time (t).

6 . The method of claim 5 , wherein the neural network includes two to seven dense layers and between 100 and 600 neurons per layer.

7 . The method of claim 1 , wherein the dynamic well data includes one or more of a resource production rate for a previous time interval, a bottom hole pressure at the target well, a shut-in bottom hole pressure at the target well, and an average draw down pressure at the target well.

8 . The method of claim 1 , further comprising:
identifying a subset of data from the historical production data associated with a shut-in period of days; and removing the subset of data associated with the shut-in period of days from the historical production data.

9 . The method of claim 1 , wherein the decline model has an elapsed days feature variable that is reset by an occurrence of an acid job or a recompletion at the target well.

10 . The method of claim 1 , wherein the input data set is generated from an initial data set filtered based on a well age or a type of well.

11 . The method of claim 1 , wherein the first time interval or the second time interval is based on a user input indicating a desired length of time, or a comparison of different lengths of time affecting an absolute percentage error of the predicted well production profile.

12 . The method of claim 1 , further comprising developing the target well based on the predicted well production profile.

13 . One or more tangible non-transitory computer-readable storage media storing computer-executable instructions for performing a computer process on a computing system, the computer process comprising:
generating a static model based on an input data set including historical production data corresponding to one or more wells; generating a decline model based on the historical production data and dynamic well data; and generating a predicted well production profile for a target well by:
calculating, using the static model and one or more well features of the target well, a predicted initial resource production rate for the target well;
calculating, using the decline model and the predicted initial resource production rate, a first final resource production rate for the target well at a first time interval; and
calculating, using the decline model and the first final resource production rate at the first time interval, a second final resource production rate at a second time interval subsequent to the first time interval.

14 . The one or more tangible non-transitory computer-readable storage media of claim 13 , wherein generating the predicted well production profile includes recursive calculations generating resource production rates for a series of time intervals.

15 . The one or more tangible non-transitory computer-readable storage media of claim 13 , wherein the static model is generated with supervised machine learning using the historical production data as feature inputs and a target variable being an initial resource production rate having a 30 days-averaged Initial Production (IP30) value.

16 . The one or more tangible non-transitory computer-readable storage media of claim 13 , wherein the decline model is generated with a neural network using the historical production data and the dynamic well data as feature inputs and a target variable being resource production rate at time (t).

17 . The one or more tangible non-transitory computer-readable storage media of claim 13 , the computer process further comprising:
identifying a subset of data from the historical production data associated with a shut-in period of days; and removing the subset of data associated with the shut-in period of days from the historical production data.

18 . The one or more tangible non-transitory computer-readable storage media of claim 13 , wherein the input data set is generated from an initial data set filtered based on a well age or a type of well.

19 . A system for predictive decline modeling for an oil well, the system comprising:
a predictive decline modeling system configured to generate a predicted well production profile for a target well, the predicted well production profile generated by determining a predicted initial resource production rate for the target well, a first final resource production rate for the target well at a first time interval, and a second final resource production rate at a second time interval subsequent to the first time interval, the predicted initial resource production rate determined using a static model and one or more well features of the target well, the first final resource production rate determined using a decline model and the predicted initial resource production rate, the second final resource production rate determined using the decline model and the first final resource production rate at the first time interval.

20 . The system of claim 19 , wherein the static model is generated based on an input data set including historical production data corresponding to one or more wells and the decline model is generated based on the historical production data and dynamic well data.

Assignees

• Conocophillips Co

Inventors

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

Classifications

• G06F30/28 Primary

  using fluid dynamics, e.g. using Navier-Stokes equations or computational fluid dynamics [CFD] · CPC title

• G06F30/27

  using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model · CPC title

• G06F2119/04

  Ageing analysis or optimisation against ageing · CPC title

• G06F2113/08

  Fluids · CPC title

• E21B43/00 Primary

  Methods or apparatus for obtaining oil, gas, water, soluble or meltable materials or a slurry of minerals from wells (applicable only to water E03B) · CPC title