Performance Prediction and Heating Parameter Optimization of Organic-Rich Shale In Situ Conversion Based on Numerical Simulation and Artificial Intelligence Algorithms

原位转化技术是一种绿色有效的开发富有机质页岩的方法。超临界二氧化碳(Sc-CO2)可作为页岩原位转化的良好加热介质。数值模拟是探索页岩原位转化过程的重要手段,但在不同工作条件下进行原位转化模拟需要大量的时间和计算成本。因此,本文提出了一种计算框架,通过将人工神经网络(ANN)与粒子群优化(PSO)相结合,快速预测页岩原位转化开发效果并优化加热参数。

结果表明,页岩原位转化过程中,干酪根的热解和烃类产物的释放主要发生在转化的前两年。在原位转化两年后,热解烃的生产曲线明显减缓。通过大量的原位转化模拟构建了数据库,并采用Pearson相关分析和随机森林方法获得了影响储层温度和烃类产量的七个主要控制因素。基于ANN的预测模型的决定系数高于97%,均方误差(MSE)低于0.3%。基本储层情况下,选择注入350−450°C的超临界二氧化碳流体,注入速度为600 m³/天,可以获得更好的开发效果。利用PSO算法对三种典型储层案例进行了加热参数优化,得到了合理的注入温度和注入速度,为页岩原位转化开发设计的有效应用提供了帮助。

CMG软件应用情况:

在本研究中,使用了CMG的STARS模块进行超临界二氧化碳辅助页岩原位转化的数值模拟。CMG STARS是一款多相、多组分,热采和蒸汽添加剂模拟器,能够有效模拟复杂的储层行为。通过CMG软件,研究了不同加热参数对页岩原位转化效果的影响,并建立了相应的数值模型。

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Abstract

In situ conversion technology is a green and effective way to realize the development of organic-rich shale. Supercritical CO2 can be used as a good heating medium for shale in situ conversion. Numerical simulation is an important means to explore the shale in situ conversion process, but it requires a lot of time and computational cost for in situ conversion simulation under different working conditions. Therefore, a computational framework for rapid prediction of shale in situ conversion development performance and heating parameter optimization is proposed by coupling artificial neural network (ANN) and particle swarm optimization (PSO). The results indicated that kerogen pyrolysis and hydrocarbon product release mainly occurred within 2 years of shale in situ conversion. The production curves of pyrolysis hydrocarbon obviously slowed after in situ conversion for 2 years. The database was constructed by a large number of in situ conversion simulations, and Pearson correlation analysis and the random forest method were adopted to obtain seven main controlling factors affecting reservoir temperature and hydrocarbon production. The determination coefficient of the obtained ANN-based prediction models is higher than 97%, and the mean square error (MSE) is lower than 0.3%. The basic reservoir case can choose to inject 350–450 °C supercritical CO2 (Sc-CO2) fluid with a rate of 600 m3/day to obtain a more promising development effect. The heating parameter optimization for three typical reservoir cases using PSO was performed, and reasonable injection temperature and injection rate were obtained. It realized accurate development prediction and rapid heating parameter optimization, which helps the effective application of shale in situ conversion development design.

作者单位:

中国石油大学(华东)深层油气国家重点实验室,

 

 

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