Potential for Evaluation of Interwell Connectivity under the Effect of Intraformational Bed in Reservoirs Utilizing Machine Learning Methods

Jinzi Liu

doi:10.1155/2020/1651549

Abstract

Machine learning method has gradually become an important and effective method to analyze reservoir parameters in reservoir numerical simulation. This paper provides a machine learning method to evaluate the connectivity between injection and production wells controlled by interlayer in reservoir. In this paper, Back Propagation (BP) and Convolutional Neural Networks (CNNs) are used to train the dynamic data with the influence of interlayer control connectivity in the reservoir layer as the training model. The dataset is trained with dynamic production data under different permeability, interlayer dip angle, and injection pressure. The connectivity is calculated by using the deep learning model, and the connectivity factor K is defined. The results show that compared with BP, CNN has better performance in connectivity, average absolute relative deviation (AARD) below 10.01% higher. Moreover, CNN prediction results are close to the traditional methods. This paper provides new insights and methods to evaluate the interwell connectivity in conventional or unconventional reservoirs.

Highlights

The interlayer has the function of an impermeable barrier or very low permeable high-resistance layer for fluid flow
Different variables are used as the input of the model, and the influence of each variable is analyzed by the average absolute relative deviation (AARD) value of the prediction results
This paper established a machine learning method to evaluate the interwell connectivity based on the dynamic production data

Summary

Introduction

The interlayer has the function of an impermeable barrier or very low permeable high-resistance layer for fluid flow. For the common methods, such as CM and MLR models, the input dynamic production data is less limited to production and pressure data This is because the traditional method is to establish a mathematical model for analysis of the connectivity coefficient to characterize connectivity. (1) Establishing a mathematical model of two-phase flow between injection and production wells, and taking the pressure, water content and injection pressure under different interlayer distribution and dip angle characteristics as input parameters (2) Based on the training dataset, BP and CNN methods were used to build a deep learning model to train Trained-K and Trained-θ (3) The connectivity is described by the calculated Trained-K and Trained-θ, and the accuracy of different BP and CNN methods is compared. The research results of this paper could provide new insight and prospects for future oilfield development

Methodology

Procedure

Results and Discussion

Method

Conclusion