Abstract
The injection of CO2 as part of the water-alternating-gas (WAG) process has been widely employed in many mature oil fields for effectively enhancing oil production and sequestrating carbon permanently inside the reservoirs. In addition to simulations, the use of intelligent tools is of particular interest for evaluating the uncertainties in the WAG process and predicting technical or economic performance. This study proposed the comprehensive evaluations of a water-alternating-CO2 process utilizing the artificial neural network (ANN) models that were initially generated from a qualified numerical data set. Totally two uncertain reservoir parameters and three installed surface operating factors were designed as input variables in each of the three-layer ANN models to predicting a series of WAG production performances after 5, 15, 25, and 35 injection cycles. In terms of the technical view point, the relationships among parameters and important outputs, including oil recovery, CO2 production, and net CO2 storage were accurately reflected by integrating the generated network models. More importantly, since the networks could simulate a series of injection processes, the sequent variations of those technical issues were well presented, indicating the distinct application of ANN in this study compared to previous works. The economic terms were also briefly introduced for a given fiscal condition which included sufficient concerns for a general CO2 flooding project, in a range of possible oil prices. Using the ANN models, the net present value (NPV) optimization results for several specific cases apparently expressed the profitability of the present enhanced oil recovery (EOR) project according to the unstable oil prices, and most importantly provided the most relevant injection schedules corresponding with each different scenario. Obviously, the methodology of applying traditional ANN as shown in this study can be adaptively adjusted for any other EOR project, and in particular, since the models have demonstrated their flexible capacity for economic analyses, the method can be promisingly developed to engage with other economic tools on comprehensively assessing the project.
Highlights
Because large volumes of crude oil are normally left underground after primary and secondary production phases and oil prices have remained low, enhanced oil recovery (EOR) technology has attracted more interest recently to produce more profit before field abandonment [1]
Dip angle usually occurs in most practical fields, and it partially partially affects the movement of the injected gas due to gravity [22], but this factor is not considered affects the movement of the injected gas due to gravity [22], but this factor is not considered in this in this model
Using suitable scheme regressions on the sample with overall errors less they excellent can be of excellent corresponding to a data, training-validation-test datathan ratio, theTherefore, models show used to predict any input within errors the assigned
Summary
Because large volumes of crude oil are normally left underground after primary and secondary production phases and oil prices have remained low, enhanced oil recovery (EOR) technology has attracted more interest recently to produce more profit before field abandonment [1]. The injection of CO2 is much less expensive in terms of fluid employment if sufficient CO2 is available; relevant designs for injection are still in dispute owing to the inevitable low sweep efficiency after gas breakthrough or improper carbon storage caused by gas leakage Due to these ongoing issues, it is important to verify the comprehensive performance of gas flooding for EOR projects with various injection designs and different reservoir characteristics. An integrated framework developed by Dai et al [19] analyzed the response surface for water-oil flow reactive transport to study the sensitivity and optimization of CO2 -EOR performance They concluded that reservoir parameters, such as depth, porosity, and permeability are crucial to control net CO2 storage, while well distance and the sequence of alternating water and CO2 injection are significant operational parameters for process design. The generated networks can be employed in other cases with similar site characteristics; in particular, the method using ANN can be used in other research areas for different predictive purposes
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