Abstract

The focus of this paper is on application of advance data analytics and deep machine learning methods for time series forecasting of injection/production data from subsurface hydrocarbon recovery processes. Injection and production data, from subsurface reservoir developed though implementation of water flooding recovery mechanism, is used with aim of developing a machine learning based forecasting methodology to eventually replace numerical simulation-based forecasting methods. Different machine learning algorithms exists for single- and multi-step time series forecasting, e.g., nonlinear regression, artificial neural network (ANN) and long-short-term-memory (LSTM) based recurrent neural networks (RNN). In this paper, RNN-LSTM algorithm is tested on real field data, comprising of a number of injection and production well patterns, and results of RNN-LSTM forecasting model are presented. Complexities related to data acquisition, analysis, and processing are also discussed in detail. The results presented in this paper are unique as it is for the first time that ML model is applied to forecast production from a tight carbonate hydrocarbon reservoir, which is developed through water flooding with help of long horizontal wells. Such an application for time series forecasting using RNN-LSTM model has never been presented, before in literature, to the best of author’s knowledge.

摘要

本文的重点是应用先进的数据分析和深度机器学习方法对来自地下碳氢化合物采收过程的注入/生产数据进行时间序列预测。通过实施注水恢复机制开发的地下油藏的注入和生产数据用于开发基于机器学习的预测方法，最终取代基于数值模拟的预测方法。存在用于单步和多步时间序列预测的不同机器学习算法，例如非线性回归、人工神经网络 (ANN) 和基于长短期记忆 (LSTM) 的循环神经网络 (RNN)。在本文中，RNN-LSTM 算法在真实的现场数据上进行了测试，包括许多注入和生产井网，并给出了 RNN-LSTM 预测模型的结果。还详细讨论了与数据采集、分析和处理相关的复杂性。本文的结果是独一无二的，因为这是首次将 ML 模型应用于预测一个致密碳酸盐岩油气藏的产量，该油气藏是在长水平井的帮助下通过水驱开发的。据作者所知，这种使用 RNN-LSTM 模型进行时间序列预测的应用之前从未在文献中出现过。它是在长水平井的帮助下通过注水开发的。据作者所知，这种使用 RNN-LSTM 模型进行时间序列预测的应用之前从未在文献中出现过。它是在长水平井的帮助下通过注水开发的。据作者所知，这种使用 RNN-LSTM 模型进行时间序列预测的应用之前从未在文献中出现过。