Abstract
The global increase in energy demand and the decreasing number of newly discovered hydrocarbon reservoirs caused by the relatively low oil price means that it is crucial to exploit existing reservoirs as efficiently as possible. Optimization of the reservoir control may increase the technical and economic efficiency of the production. In this paper, a novel algorithm that automatically determines the intelligent control maximizing the NPV of a given production process was developed. The idea is to build an auto-adaptive parameterized decision tree that replaces the arbitrarily selected limit values for the selected attributes of the decision tree with parameters. To select the optimal values of the decision tree parameters, an AI-based optimization tool called SMAC (Sequential Model-based Algorithm Configuration) was used. In each iteration, the generated control sequence is introduced into the reservoir simulator to compute the NVP, which is then utilized by the SMAC tool to vary the limit values to generate a better control sequence, which leads to an improved NPV. A new tool connecting the parameterized decision tree with the reservoir simulator and the optimization tool was developed. Its application on a simulation model of a real reservoir for which the CCS-EOR process was considered allowed oil production to be increased by 3.5% during the CO2-EOR phase, reducing the amount of carbon dioxide injected at that time by 16%. Hence, the created tool allowed revenue to be increased by 49%.
Highlights
Due to the reduction of hydrocarbon resources caused by increasing consumption as well as difficulties in discovering new hydrocarbon reservoirs, more and more research is focused on the effective exploitation of existing reservoirs
As a result of intelligent reservoir control determined with the use of the developed tool, the parameters of the CCS-EOR process were set at 210 bar and 9 m3 /day
A novel tool that enables the determination of intelligent reservoir control for a given production process, which brings the highest possible profit, was created
Summary
Due to the reduction of hydrocarbon resources caused by increasing consumption as well as difficulties in discovering new hydrocarbon reservoirs, more and more research is focused on the effective exploitation of existing reservoirs. The problem of optimal control of the oil and gas reservoir is to determine the control u, which maximizes the return on investment in the form of a functional described by Equation (9). Most of the methods used for solving the problem of optimal control are impractical for optimizing the production of hydrocarbon reservoirs In this case, it is impossible to functionally link the optimized quality indicator and decision variables. To model such a process, application of a complex simulation model that considers the process of mixing carbon dioxide with oil and allows the CO2 flow to be monitored is required Optimal control of such a process is not a trivial problem. The aim of the work reported here was to develop a novel tool that automatically determines intelligent control of real hydrocarbon reservoirs maximizing the NPV of a given production process.
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