Integration of Response Surface and Decision Support to optimize a Well Sidetrack under Uncertainty II. Secondary Recovery Mechanism
Abisoye M. Mumuni1, Oyinkepreye O. Orodu2, Sunday S. Ikiensikimama3
1Abisoye M. Mumuni, (Dalhousie University) & PhD Student, CEFOR-ACE, World-Bank University of Port-Harcourt, Nigeria.
2Oyinkepreye O. Orodu, Professor, Head of Department, Petroleum Engineering, Covenant University, Nigeria.
3Sunday S. Ikiensikimama , Associate Professor, Department of Petroleum & Gas Engineering, University of Port Harcourt, Nigeria.
Manuscript received on November 20, 2019. | Revised Manuscript received on November 28, 2019. | Manuscript published on 30 November, 2019. | PP: 6571-6583 | Volume-8 Issue-4, November 2019. | Retrieval Number: D8333118419/2019©BEIESP | DOI: 10.35940/ijrte.D8333.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The optimal time to sidetrack into a different layer from an already producing horizon with secondary recovery mechanism of waterflooding is evaluated with the uncertainties embedded in Probabilities of success (POS) including economic, operational, technical and reservoir properties. Previous literatures are majorly primary recovery and secondary recovery by waterflooding in which production profiles were represented by empirical and analytical models. However, not all recovery mechanisms can be sufficiently reproduced by these models and this introduces and explains the need for the use of proxy models to predict cumulative production and net-present-value (NPV). The peculiarity of this study is the application of decision analysis/tree with multiple terminal branches to both production and injection sidetrack, where NPV is estimated under the influence of change of recovery mechanism due to sidetrack (recompletion) to another possibly non-communicating zone or layer with uncertainty of reservoir properties and production discontinuity from the already producing horizon. By and large, sidetrack time adds in acute non-linearity on the NPV. Multi–objective functions of proxy models over time-intervals for the impacted terminal branches, known as split design was applied to evaluate when to carry out a well sidetrack operation under risk and uncertainty. This was adopted to resolve severe non-linearity of the NPV and the multi-objective function of EMV by a standard optimization algorithm in a spreadsheet. The final results gave a satisfactory match to the simulation results. In order to get a perfect match through more improvements on performance there is a need for large computation times and the decision must be made depending on the required result. Monte- Carlo simulation analysis shows that optimal sidetrack time is at the early production life.
Keywords: Sidetrack, Multi-Objective Optimization, Experimental Design, Decision Analysis, Net-Present-Value.
Scope of the Article: Discrete Optimization.