Understanding the Controlling Factors for CO₂ Sequestration in Depleted Shale Reservoirs Using Data Analytics and Machine Learning

Abstract

Carbon capture and sequestration is the process of capturing carbon dioxide (CO 2 ) from refineries, industrial facilities, and major point sources such as power plants and storing the CO 2 in subsurface formations. Carbon capture and sequestration has the potential to generate an industry comparable to, if not greater than, the existing oil and gas sector. Subsurface formations such as unconventional oil and gas reservoirs can store significant quantities of CO 2 . Despite their importance in the oil and gas industry, our understanding of CO 2 sequestration in unconventional reservoirs still needs to be developed. The objective of this paper was to use an extensive data set of numerical simulation results combined with data analytics and machine learning to identify the key parameters that affect CO 2 sequestration in depleted shale reservoirs. Machine learning-based predictive models based on multiple linear regression, regression tree, bagging, random forest, and gradient boosting were built to predict the cumulative CO 2 injected. Variable importance was carried out to identify and rank important reservoir and operational parameters. The results showed that random forest provided the best predictive ability among the machine learning techniques and that regression tree had the worst predictive ability, mainly because of overfitting. The most significant variable for predicting cumulative CO 2 sequestration was stimulated reservoir volume fracture permeability. The workflows, machine learning models, and results reported in this study provide insights for exploration and production companies interested in quantifying CO 2 sequestration performance in shale reservoirs.

Development of Empirical and Artificial Neural Network Model for the Prediction of Sorption Time to Assess the Potential of CO2 Sequestration in Coal

“…Machine learning techniques such as ANN have been utilized in solving complex nonlinear problems for CBM gas content prediction; reservoir analysis and prediction of production from well; – prediction of key reservoir properties; prediction of coal properties; and analysis of factors affecting productivity of CBM and factors controlling CO 2 sequestration …”

“…Machine learning techniques such as ANN have been utilized in solving complex nonlinear problems for CBM gas content prediction; 36 reservoir analysis and prediction of production from well; 37−40 prediction of key reservoir properties; 41 prediction of coal properties; 42 and analysis of factors affecting productivity of CBM 43 and factors controlling CO 2 sequestration. 44 Therefore, attempts are further made to explore the utilization of artificial intelligence tools such as ANN, which may provide a better prediction of sorption time as compared to a linear regression model. ANN is capable of identifying nonlinear relationships between the inputs and target which may further reduce the errors.…”

Enhanced Oil Recovery and CO2 Storage by Enhanced Carbonated Water Injection: A Mini-Review

“…The CO 2 storage primarily originates from refineries, industrial facilities, and power plants. 75 Additionally, natural CO 2 gas fields constitute one of the primary sources of CO 2 during the development of oil and gas reservoirs. Compared with CO 2 injection, CO 2 is dissolved in both the water and oil phases during CW injection, which reduces CO 2 transport within the reservoir.…”

“…CO 2 storage in reservoirs can occur in two forms: First, during the development process, where CO 2 is injected to enhance recovery and achieve CO 2 storage; second, in depleted reservoirs, where CO 2 is injected using existing wellheads, pipelines, and other infrastructure upon reaching the exhaustion stage. The CO 2 storage primarily originates from refineries, industrial facilities, and power plants . Additionally, natural CO 2 gas fields constitute one of the primary sources of CO 2 during the development of oil and gas reservoirs.…”

Hydrogen sulphide

“…For simplicity and economic reasons, wellhead injection temperatures and pressures are usually aligned with that of the compressed gas's transportation conditions [90] although temperatures can range between 30°C all through to 200°C and pressures from 1 to 30 MPa, with both temperature and pressure ceiling usually determined by the reservoir's state [91], and the acid gas injection strategy delineated to take advantage of system thermodynamics, including importantly, use of the phase envelope [92]. Storage is usually implemented in deep saline aquifers, depleted oil and gas reservoirs, clastic and basaltic formations, unmineable coal seams [93], and unconventional oil and gas reservoirs [94]. The temperatures are non-trivial during injection, as they affect the reservoir fluids and rock thermodynamically, chemically, hydrologically and mechanically, wherein the Joule-Thomson cooling effect may occur due to advection and expansion of injected fluids [90].…”