Application of Machine Learning in Screening the Optimal Enhanced Oil Recovery Technique
Authors: Madhumaya A. , Vyas A. 
Published: October 2024

The exploration and production of hydrocarbons pose numerous difficulties such as addressing the growing energy demand, ensuring sustainable operations, managing declining in production from mature reservoirs, and the increasing difficulty of accessing new reservoirs. Oil recovery techniques that include natural drive energy of reservoirs, artificial lift techniques, and pressure maintenance methods can extract approximately one-third of the original oil in place (OOIP) (Walsh & Lake, 2003). The primary goal of Enhanced Oil Recovery (EOR) techniques is to recover the residual oil in the reservoir. This is accomplished by altering the properties of the rock and/or fluids through the injection of agents such as chemical compounds dissolved in water, steam, or gases that are miscible with the residual hydrocarbon. As a result, the overall hydrocarbon recovery is increased. Nevertheless, the exorbitant expense associated with implementing existing EOR techniques limits the amount of recoverable hydrocarbons, a factor directly linked to the price of crude oil. In addition, numerous countries that produce hydrocarbons have a prerequisite for conducting successful pilot EOR projects before field implementation. Moreover, while modeling EOR pilots for fields that have substantial heterogeneity and anisotropy (associated to large and complex reservoirs), the time and costs associated with simulation and computation are significantly elevated. The lack of accurate information in assumed data introduces uncertainty, which undermines the reliability of the pilot study results.
To address these issues, recent research has utilized Machine Learning (ML) techniques to predict incremental hydrocarbon recovery under various EOR schemes for a new reservoir (Aminzadeh et al., 2022). This prediction is based on available data involving the reservoir's location, lithology, geophysical properties, and fluid pressure-volume-temperature (PVT) behaviour. Data-driven methodologies have made significant contributions across a wide range of industries. Although machine learning has gained widespread recognition for its utility across various sectors, its full potential within the oil and gas industry remains untapped. Machine learning and deep learning models have been utilized to tackle challenges in multiphase production from wellbores (Ristanto, 2018) and analyse data obtained from bottomhole pressure and flow rate gauges (Liu & Horne, 2013; Tian, 2014). The use of data analytics in production operations addresses the shortcomings in existing methodologies. Contrary to traditional reservoir simulators, ML offers numerous benefits, such as the ability to handle multi-dimensional and multi-variety data in dynamic or uncertain environments, reduction in workforce and time due to automation, the flexibility for users to choose modelling algorithms, and the constant evolution of ML as a future technology. The objective of this study is to create an EOR screener using a novel data-driven workflow. This workflow will use available rock and fluid properties to predict the most appropriate EOR scheme for a reservoir. The goal is to reduce computational time while maintaining accuracy.