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CN-121803197-B - Natural gas hydrate reservoir layer equilibrium exploitation regulation and control method based on simulation optimization

CN121803197BCN 121803197 BCN121803197 BCN 121803197BCN-121803197-B

Abstract

The invention discloses a natural gas hydrate reservoir layered balanced exploitation regulation method based on simulation optimization, which comprises the steps of S1, constructing a three-dimensional geological model according to logging, earthquake and core data of a target area, S2, establishing a thermal-flow-force-chemical multi-field coupling numerical model, initializing, S3, defining a multi-dimensional engineering decision parameter space, S4, establishing a comprehensive objective function, S5, obtaining an optimal engineering decision parameter combination X_optimal through iterative search, S6, implementing drilling and completion operations according to the X_optimal, and dynamically verifying and feedback adjusting the model and exploitation system based on real-time monitoring data in the production process. The thermal-flow-force-chemical multi-field coupling numerical model is built based on the three-dimensional geological model, the agent model and multi-objective optimization are introduced, dynamic verification and feedback adjustment are carried out by combining monitoring data during production, interlayer contradiction is effectively restrained, balanced utilization of reservoir energy is realized, and the overall recovery ratio and development economy of the multi-layer hydrate reservoir are improved.

Inventors

  • Lv Yunshu
  • XU YANQING
  • FAN MENG
  • YAN CHAOYUE
  • Gan Bicheng
  • XUAN YINGLONG
  • SUN YUXUE
  • ZHANG HAIXIANG
  • WANG DIANJU
  • LIU SHUFEN
  • LI ZHANDONG

Assignees

  • 东北石油大学三亚海洋油气研究院
  • 东北石油大学

Dates

Publication Date
20260508
Application Date
20260310

Claims (4)

  1. 1. A natural gas hydrate reservoir layer equilibrium exploitation regulation method based on simulation optimization is characterized by comprising the following steps: S1, constructing a three-dimensional geological model according to logging, earthquake and core data of a target area; s2, establishing a thermal-flow-force-chemical multi-field coupling numerical model based on the three-dimensional geological model, and initializing; s3, defining a multidimensional engineering decision parameter space; s4, establishing a comprehensive objective function; s5, obtaining an optimal engineering decision parameter combination X_optimal through iterative search; S6, implementing drilling and well completion operation according to the X_optimal, and dynamically verifying and feedback adjusting the model and the exploitation system based on real-time monitoring data in the production process; in the step S4, a comprehensive objective function is established according to the maximization of the total accumulated gas production, the interlayer gas production balance degree and the economic benefit index, ; Wherein, the 、 And As a parameter of the weight-bearing element, For the gas production balance between the layers, For a normalized total cumulative gas production, Is normalized net present value; Normalized total accumulated gas yield The calculation formula of (2) is as follows: ; Wherein, the To simulate the actual total cumulative gas production obtained, Is a theoretical recoverable quantity estimated from a geological reserve; interlayer gas production balance degree The calculation formula of (2) is as follows: ; Wherein, the Gas production for each layer Is set in the standard deviation of (2), Gas production for each layer Is used for the arithmetic mean of (a), Is the first Cumulative gas production from individual hydrate reservoirs throughout the production cycle, =1, 2.,. N is a number of the N, N is the total number of producing layers; Normalized net present value The calculation formula of (2) is as follows: ; Wherein, the In order to be able to take time, For the cash inflow of the t-th year, For cash flow in the t-th year, Is the discount rate.
  2. 2. The natural gas hydrate reservoir layer equilibrium exploitation regulating method based on simulation optimization according to claim 1, wherein in the step S5, the method further comprises the following steps: S51, generating an initial parameter sample set, calling a multi-field coupling numerical model for each parameter sample to obtain a corresponding objective function value, and constructing a sample database; s52, obtaining corresponding objective function values through a multi-field coupling numerical model by the parameter combination of each sample point, and forming a sample database of parameter combination-simulation results; s53, training a simulation result of the sample set through a machine learning algorithm to obtain a proxy model; S54, taking the agent model as a rapid evaluator to obtain the optimal engineering decision parameter combination X_optimal.
  3. 3. The method for balanced production and control of natural gas hydrate reservoir layers based on simulation optimization according to claim 2, wherein in the step S51, a certain number of sample points are extracted in an engineering decision parameter space by adopting an orthogonal experimental design method to generate an initial parameter sample set.
  4. 4. The natural gas hydrate reservoir layer balance exploitation regulation and control method based on simulation optimization according to claim 3, wherein in the step S6, real-time monitoring data are fed back to a multi-field coupling numerical model, and inversion correction is carried out on uncertainty parameters in the model, so that the model always keeps accurate mapping of underground conditions.

Description

Natural gas hydrate reservoir layer equilibrium exploitation regulation and control method based on simulation optimization Technical Field The invention relates to the technical field of oil and gas field development, in particular to a natural gas hydrate reservoir layer equilibrium exploitation regulation method based on simulation optimization. Background Natural gas hydrate is a potential clean energy source which is endowed in sea areas or frozen soil areas, and the depressurization exploitation process involves complex physical processes such as hydrate decomposition phase change, multiphase seepage, heat transfer, sediment mechanical response and the like. In a multilayer natural gas hydrate reservoir, due to remarkable heterogeneity of reservoirs in porosity, permeability, net-wool ratio and vertical distribution thereof, a traditional single depressurization system or indistinguishable overall exploitation mode is adopted, a hypertonic layer or a layer section with better connectivity is prone to depressurization preferentially and gas production rapidly, a hypotonic layer is difficult to effectively use, and therefore problems of serious interlayer interference, low overall recovery ratio, high water yield, increased risk of reservoir stability and the like are caused. In the prior art, most methods focus on numerical simulation analysis of single-layer hydrate reservoirs or perform one-time scheme optimization only before exploitation, and lack a systematic method capable of continuously modifying a model of a multi-layer hydrate reservoir and performing layered dynamic regulation in combination with real-time monitoring data in the production process. Disclosure of Invention The invention aims to overcome the defects of the prior art and provides a natural gas hydrate reservoir layer balanced exploitation regulation method based on simulation optimization. The invention aims at realizing the following technical scheme that the natural gas hydrate reservoir layer balance exploitation regulation and control method based on simulation optimization comprises the following steps: S1, constructing a three-dimensional geological model according to logging, earthquake and core data of a target area; s2, establishing a thermal-flow-force-chemical multi-field coupling numerical model based on a three-dimensional geological model, and initializing; s3, defining a multidimensional engineering decision parameter space; s4, establishing a comprehensive objective function; s5, obtaining an optimal engineering decision parameter combination X_optimal through iterative search; S6, drilling and well completion operations are implemented according to the X_optimal, and dynamic verification and feedback adjustment are carried out on the model and the exploitation system based on real-time monitoring data in the production process. Preferably, in step S4, a comprehensive objective function is established according to the maximization of total accumulated gas production, the balance of interlayer gas production and the economic benefit index, ; Wherein, the 、AndAs a parameter of the weight-bearing element,For the gas production balance between the layers,For a normalized total cumulative gas production,Is normalized net present value; Normalized total accumulated gas yield The calculation formula of (2) is as follows: ; Wherein, the To simulate the actual total cumulative gas production obtained,Is a theoretical recoverable quantity estimated from a geological reserve; interlayer gas production balance degree The calculation formula of (2) is as follows: ; Wherein, the Gas production for each layerIs set in the standard deviation of (2),Gas production for each layerIs used for the arithmetic mean of (a),First, theCumulative gas production from individual hydrate reservoirs throughout the production cycle,=1, 2.,. N is a number of the N, N is the total number of producing layers; Normalized net present value The calculation formula of (2) is as follows: ; Wherein, the In order to be able to take time,Cash flows in the t-th year,Cash flows out in the t-th year,Is the discount rate. Preferably, in step S5, the method further comprises the steps of: S51, generating an initial parameter sample set, calling a multi-field coupling numerical model for each parameter sample to obtain a corresponding objective function value, and constructing a sample database; s52, obtaining corresponding objective function values through a multi-field coupling numerical model by the parameter combination of each sample point, and forming a sample database of parameter combination-simulation results; s53, training a simulation result of the sample set through a machine learning algorithm to obtain a proxy model; S54, taking the agent model as a rapid evaluator to obtain the optimal engineering decision parameter combination X_optimal. Preferably, in step S51, an orthogonal experiment design method is adopted to extract a certain number of