Effective Prediction and Management of a CO2 Flooding Process for Enhancing Oil Recovery Using Artificial Neural Networks

Abstract

The injection of CO2 has been in global use for enhanced oil recovery (EOR) as it can improve oil production in mature fields. It also has environmental benefits for reducing greenhouse carbon by permanently sequestrating CO2 (carbon capture and storage (CCS)) in reservoirs. As a part of numerical studies, this work proposed a novel application of an artificial neural network (ANN) to forecast the performance of a water-alternating-CO2 process and effectively manage the injected CO2 in a combined CCS–EOR project. Three targets including oil recovery, net CO2 storage, and cumulative gaseous CO2 production were quantitatively simulated by three separate ANN models for a series of injection frames of 5, 15, 25, and 35 cycles. The concurrent estimations of a sequence of outputs have shown a relevant application in scheduling the injection process based on the progressive profile of the targets. For a specific surface design, an increment of 5.8% oil recovery and 4% net CO2 storage was achieved from 25 cycles to 35 cycles, suggesting ending the injection at 25 cycles. Using the models, distinct optimizations were also computed for oil recovery and net CO2 sequestration in various reservoir conditions. The results expressed a maximum oil recovery from 22% to 30% oil in place (OIP) and around 21,000–29,000 tons of CO2 trapped underground after 35 cycles if the injection began at 60% water saturation. The new approach presented in this study of applying an ANN is obviously effective in forecasting and managing the entire CO2 injection process instead of a single output as presented in previous studies.

Governance, CO2 emissions and inclusive human development in sub-Saharan Africa

“…2 This stream therefore focuses on the nexus between environmental degradation and economic growth. The second stream has two main branches: (i) connections between the consumption of energy and environmental pollution (Ang, 2007;Apergis and Payne, 2009;Begum et al, 2015;B€ olu¨k and Mehmet, 2015;Cui et al, 2018;Jumbe, 2004;Le Van and Chon, 2017;Menyah and Wolde-Rufael, 2010;Odhiambo, 2009aOdhiambo, , 2009bOzturk and Acaravci, 2010;Rui et al, 2018) and (ii) linkages between energy consumption and economic growth (see Esso, 2010;Mehrara, 2007). 3 Noticeably, a common shortcoming in the engaged literature is the fact that providing nexuses between indicators of macroeconomic development are not enough to effectively inform policy makers.…”

Influencing factors and application prospects of CO2 flooding in heterogeneous glutenite reservoirs

“…In addition to physical experiments, many component models used for CO 2 flooding have been established, and numerical simulation technology has also been widely used in the evaluation and prediction of the effects of CO 2 flooding. By establishing the two-dimensional plane model and three-dimensional model and using computers to simulate the displacement process, the efficiency of CO 2 flooding and its influencing factors under different conditions can be evaluated, which can provide reasonable guidance for the implementation of carbon dioxide flooding [34][35][36][37] . The heterogeneity has a great influence on the EOR (Enhanced oil recovery) effect of CO 2 flooding, the fluid distribution in the process of oil displacement has a good correlation with the permeability of rocks.…”

Real-Time Prediction of the Rheological Properties of Water-Based Drill-In Fluid Using Artificial Neural Networks

“…Their model investigated the influences of reservoir parameters (such as reservoir size, thickness, reservoir heterogeneity, and permeability ratio) on ICD completion performance. Van and Chon [30,31] evaluated the performance of carbon dioxide (CO 2 ) flooding using ANN techniques. They developed ANN models for determining oil production rate, CO 2 production, and gas-oil ratio (GOR).…”