Automatic history matching system and method for an oil reservoir based on transfer learning

Patent metadata

Field	Value
Publication number	US-12560069-B2
Application number	US-202117540861-A
Country	US
Kind code	B2
Filing date	Dec 2, 2021
Priority date	Nov 25, 2020
Publication date	Feb 24, 2026
Grant date	Feb 24, 2026

Abstract

The present invention relates to an automatic history matching system for an oil reservoir based on transfer learning, comprising a data reading module, a population reinitializing module, an optimization module, a simulated calculation module, a comparative judgment module and an output module, wherein the data reading module reads an optimized result of an existing oil reservoir, outputs the optimized result to the population reinitializing module, obtains an initial population of a new oil reservoir by calculation and outputs the initial population to the optimization module.

First claim

The invention claimed is:

1 . An automatic history matching method for an oil reservoir based on transfer learning, adopting an automatic history matching system for an oil reservoir for improving the accuracy of reservoir forecasts in oil engineering, characterized in that the method comprises the steps of:
S1, reading observed data and an optimized result of an old model; S2, performing population reinitializing processing on the optimized result of the old model to generate a reinitialized population; and S3, performing evolutionary optimization calculation and simulated calculation by using the reinitialized population, predicting oil reservoir dynamically according to oil reservoir numerical simulator; characterized in that S1 comprises the specific steps of: assuming that the current moment is the moment (t+1), wherein the history matching model used by the oil reservoir at present is a t moment model, and as the oil reservoir at the moment (t+1) changes or the oil reservoir model is added with novel data, it is necessary to construct a history matching model for a current situation of the oil reservoir at the moment (t+1); reading the observed data of the oil reservoir at the moment (t+1) and providing preparation to judge whether the optimized result meets the requirement; and reading the optimized results of the history matching models at the moment (t−2), the moment (t−1) and the moment t, and constructing the initial population for the optimization process at the moment (t+1) by using its useful information for reference; wherein the automatic history matching method is based on transfer learning, obtaining simulated oil production rates and liquid production rates for predicting future production status in oil engineering, for improving the accuracy of reservoir forecasts in oil engineering.

2 . The automatic history matching method for an oil reservoir based on transfer learning according to claim 1 , characterized in that S2 comprises the specific steps of: processing the optimized result of the history matching model of the oil reservoir at the moment t by using a reinitializing policy with directivity and random change based on transfer learning; extracting experience used as reference thereof; and constructing an initial population of a case at the moment (t+1).

3 . The automatic history matching method for an oil reservoir based on transfer learning according to claim 1 , characterized in that S3 comprises the specific steps of: setting an optimized target function that meets a specific requirement of the case at the moment (t+1), specific optimization indexes comprising, for example, oil production, water production and water content, and a formula (1) giving a loss function of the history matching model of a reservoir:
{ M =Σ( Q obs− Q cal)2}  (1)
where M is an unmatched value, Qobs is true observed data of the oil reservoir, and Qcal is simulated calculation data by using the population obtained in the S2 as the initial population in the optimization process of the history matching model of the oil reservoir at the moment (t+1), using PSO or NSGA-III evolutionary optimization algorithm to optimize the history matching model of the oil reservoir at the moment (t+1) and adjusting the parameter thereof to obtain the optimized result at the moment (t+1); and putting the optimized result into a reservoir numerical simulation for simulated calculation, wherein if an error between the observed data and the simulated data meets the requirement, an effective effect may be outputted, and on the contrary, iterative calculation is performed continuously.

4 . The automatic history matching method for an oil reservoir based on transfer learning according to claim 1 , characterized in that S2 specifically comprises: S21, controlled translation
calculating centroids of the optimum solution sets at three moments: C(t−2), C(t−1) and C(t) respectively according to optimum solution sets at the moment (t−2), the moment (t−1) and the movement t, making C(t)−C(t−1) to obtain a vector d(t), and making C(t−1)−C(t−2) to obtain a vector d(t−1); making C(t−1)+d(t+1) to obtain a vector b, defining an included angle between b and d(t) as cos−1cd, and calculating cd and further calculating a translation vector cdd(t), wherein all solutions in the optimum solution set at the moment t, i.e., all individuals and translation vectors in the optimized population at the moment t are added to reposition according to amplitude and direction of the translation vectors; tracking movement of the optimum solution sets at the moment (t−2), the moment (t−1) and the moment t, thereby providing experience of being transferred to the optimum solution set that predicts a new problem at the moment (t+1); first, calculating movement of the optimized population individual at the moment t, wherein as shown in a formula (2), C(t) and C(t−1) are centroids of the population individual at the moment t and the moment (t−1);
dt=C ( t )− C ( t− 1)  (2)

a predicted value C(t+1) is a centroid of the population individual in the optimums solution set at the moment (t+1), wherein a calculation method thereof is as shown in a formula (3),

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Assignees

• Univ China Petroleum East China

Inventors

• Zhang Kai
• Yao Jun
• Li Guoxin
• Yan Xia
• Liu Bingxuan
• Liu Junrong
• Wang Wei
• Zhao Hui
• Yao Chuanjin
• Li Yanlai
• Wang Zhiwei
• Qi Ji
• Ma Xiaopeng
• Jiang Yunqi

Classifications

• G01V20/00

  Geomodelling in general · CPC title

• G06F17/11

  for solving equations {, e.g. nonlinear equations, general mathematical optimization problems (optimization specially adapted for a specific administrative, business or logistic context G06Q10/04)} · CPC title

• E21B2200/20

  Computer models or simulations, e.g. for reservoirs under production, drill bits · CPC title

• G06F2111/10

  Numerical modelling · CPC title

• G06Q10/04

  Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" (market predictions or forecasting for commercial activities G06Q30/0202) · CPC title