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

Patent metadata

Field	Value
Publication number	US-2022341306-A1
Application number	US-202117540861-A
Country	US
Kind code	A1
Filing date	Dec 2, 2021
Priority date	Nov 25, 2020
Publication date	Oct 27, 2022
Grant date	—

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; the optimized result is outputted to the simulated calculation module to obtain oil reservoir production simulated data, and the oil reservoir production simulated data is outputted to the comparative judgment module; when an error between the simulated data and observed data meets the requirement, the optimized result is outputted to the output module, and the system operation is completed; and if the error does not meet the requirement, optimization will be performed again. The present invention may construct the initial population closer to the optimized result by using experience of adjusting a history matching model in an old example oil reservoir model according to the matching experience of an existing model, and can be integrated with any one evolutionary optimization algorithm, so the system is more suitably applied to an actual engineering problem.

First claim

1 . 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; and characterized in that: 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; the optimized result is outputted to the simulated calculation module to obtain oil reservoir production simulated data, and the oil reservoir production simulated data is outputted to the comparative judgment module; when an error between the simulated data and observed data meets the requirement, the optimized result is outputted to the output module, and the system operation is completed; and if the error does not meet the requirement, the simulated result is outputted to the optimization module, and optimized calculation of the optimization module, the simulated calculation module and the comparative judging module is performed again.

2 . The automatic history matching system for an oil reservoir based on transfer learning according to claim 1 , characterized in that:
the data reading module is configured to read oil reservoir observed data at a moment (t+1) and the optimized results of the history matching model at a moment (t−2), a moment (t−1) and a moment t, and output the observed data and optimized results to the population reinitializing module; the population reinitializing module is configured to process the optimized result at the moment t by using a reinitializing policy with directivity and random change based on transfer learning at the moment (t+1) to obtain the initial population using experience of adjusting a history matching model in an old optimized example, which is used for the optimization module to optimize the oil reservoir model at the moment (t+1) subsequently; the optimization module is configured to optimize according to the oil reservoir observed data and the initial population at the moment (t+1) to obtain a static parameter of an oil reservoir model optimized at the current moment and output the static parameter to the subsequent simulated calculation module; the simulated calculation module is configured to perform numerical simulation calculation on the static parameter to the subsequent simulated calculation module obtained by the optimization module to obtain a simulated production result, i.e., simulated data, and output the simulated production result to the comparative judging module; the comparative judging module is configured to compare the simulated data with the observed data at the current moment to obtain an error, and judge whether the error is lower than a preset error value; if yes, go to the output module, and if not, go to the optimization module to perform iterative calculation again; and the output module is configured to output the optimized static parameter of the oil reservoir history matching model, i.e., a final optimized result when the error is lower than the preset error value.

3 . The automatic history matching system for an oil reservoir based on transfer learning according to claim 1 , characterized in that: the population reinitializing module specifically comprises a controlled translation unit, a directional variation unit and a random variation unit, wherein
the controlled translation unit is configured to calculate a translation vector c d d (t) and translate the optimized result at the moment t, i.e., an optimized population, to obtain a repositioned population individual; the directive vacation unit is configured to calculate a random vector N(1, σ d 2 ) and perform random variation on all the population individuals processed by the controlled translation unit in a direction of the translation vector; and the random variation unit is configured to calculate an average distance d pw between paired solutions and apply random variation to all the population individuals processed by the controlled translation unit and the directional variation unit to form an example initial population at the moment (t+1).

4 . The automatic history matching system for an oil reservoir based on transfer learning according to claim 1 , characterized in that: the optimization module specifically comprises a construction unit, an initialization unit, an updating unit, a judging unit and an optimum value output unit, wherein
the construction unit is configured to construct a target function (a loss function) of the oil reservoir, the target function being determined according to a specific history matching model construction requirement of the oil reservoir; the initialization unit is configured to initialize the parameter and set an optimization stopping condition, the parameter at least comprising a number of iterations and a population scale; the updating unit is configured to update a reference point and a population according to a preset algorithm rule condition; the judging unit is configured to judge whether the optimization stopping condition is met; if yes, go to the optimum value output unit, and if not, go to the updating unit; and the optimum value output unit is configured to output to the simulated calculation module the optimum target function value and the optimized static parameter of the oil reservoir corresponding to the optimum target function value.

5 . An automatic history matching method for an oil reservoir based on transfer learning, adopting the automatic history matching system for an oil reservoir based on transfer learning according to any of claim 1 , 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; and S3, performing evolutionary optimization calculation and simulated calculation by using the reinitialized population.

6 . The automatic history matching method for an oil reservoir based on transfer learning according to claim 5 , characterized in that S1 comprises the specific steps of: realized by the data reading module in the automatic history matching system for an oil reservoir based on transfer learning; 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 the 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.

7 . The automatic history matching method for an oil reservoir based on transfer learning according to claim 5 , characterized in that S2 comprises the specific steps of: realized by the reinitializing module in the automatic history matching system for an oil reservoir based on transfer learning; 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 ca

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

• E21B2200/20

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

• 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

• G06N3/006 Primary

  based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO] · CPC title

• G06F2111/10

  Numerical modelling · CPC title