Machine Learning to Rate and Predict the Efficiency of Waterflooding for Oil Production

Abstract

Waterflooding is a widely used secondary oil recovery technique. The oil and gas industry uses a complex reservoir numerical simulation and reservoir engineering analysis to forecast production curves from waterflooding projects. The application of such standard methods at the stage of assessing the potential of a huge number of projects could be computationally inefficient and requires a lot of effort. This paper demonstrates the applicability of machine learning to rate the outcome of waterflooding applied to an oil reservoir. We also explore the relationship of project evaluations by operators at the final stages with several performance metrics for forecasting. Real data about several thousand waterflooding projects in Texas are used in the current study. We compare the ML models rankings of the waterflooding efficiency and the expert rankings. Linear regression models along with neural networks and gradient boosting on decision threes are considered. We show that machine learning models allow reducing computational complexity and can be useful for rating the reservoirs, with respect to the effectiveness of waterflooding.

Oil and Gas Production Forecasting Using Decision Trees, Random Forst, and XGBoost

“…Indeed, mean squared error is highly effective at assigning greater importance to data points. When the model produces an abysmal forecast, the MSE function's squaring component amplifies the error's magnitude [40] by using Equation (5).…”

“…It is represented in 30.1K rows and 20 columns. Figure (5) provides part of the Oil and Gas dataset, specifically showcasing the data trends for active oil wells, inactive oil wells, active Gas wells, injection wells, and disposal wells.…”

“…Production forecasting is a crucial process for governmental and organizational entities, enabling them to formulate requisite economic strategies [4]. Forecasting output in the oil and gas sector requires a complicated numerical simulation of reservoirs and their engineering study [5]. The precise prediction is a substantial and different undertaking aimed at monitoring and enhancing Oil reservoirs.…”

Integrated Waterflooding Effect Evaluation Methodology for Carbonate Fractured–Vuggy Reservoirs Based on the Unascertained Measure–Mahalanobis Distance Theory

“…In addition, based on the reservoir numerical model, and incorporated with the interwell tracer technology, Guo et al [16] evaluated the balanced displacement effect during the reservoir waterflooding development process, according to the interwell areal sweep efficiency estimated by the tracer types and their monitoring amounts in the injection wells, and enabled the rapid assessment of the offshore reservoir waterflooding development effect. Makhotin et al [17] assessed the practicability of machine-learning techniques in the evaluation of the reservoir waterflooding development effects. Scale precipitation can have a detrimental influence on the development effect during waterflooding, which can be inhibited via the response surface methodology [18].…”

A Review on Intelligent Control Theory and Applications in Process Optimization and Smart Manufacturing

“…These advantages extend beyond the applications listed in Table 5, offering competitive edge in the global market. energy consumption changes in product specifications [130] costs manufacturing producing battery cells [131] automotive cost control within supply chain [132] production efficiency energy waterflooding for oil production [133] manufacturing wire arc additive manufacturing [134] product quality tube hydroforming estimate product parameters [135] food freshness inspection [136]…”