Hybrid and Automated Machine Learning Approaches for Oil Fields Development: the Case Study of Volve Field, North Sea

TL;DR

This paper explores hybrid and automated machine learning techniques for optimizing oil field development, focusing on well placement and reservoir analysis using the Volve field case study.

Contribution

It introduces hybrid physics-based and machine learning models for oil production forecasting and automates seismic analysis with neural networks for reservoir detection.

Findings

Hybrid models improve oil production estimation accuracy.

Automated seismic analysis accelerates reservoir detection.

Machine learning methods outperform traditional physics-based models.

Abstract

The paper describes the usage of intelligent approaches for field development tasks that may assist a decision-making process. We focused on the problem of wells location optimization and two tasks within it: improving the quality of oil production estimation and estimation of reservoir characteristics for appropriate wells allocation and parametrization, using machine learning methods. For oil production estimation, we implemented and investigated the quality of forecasting models: physics-based, pure data-driven, and hybrid one. The CRMIP model was chosen as a physics-based approach. We compare it with the machine learning and hybrid methods in a frame of oil production forecasting task. In the investigation of reservoir characteristics for wells location choice, we automated the seismic analysis using evolutionary identification of convolutional neural network for the reservoir detection. The Volve oil field dataset was used as a case study to conduct the experiments. The implemented approaches can be used to analyze different oil fields or adapted to similar physics-related problems.