Sidetrack/Recompletion Time Evaluation by Proxy Model

Abstract

Sidetrack during field development and ongoing production arises to exploit bypassed reserves (unswept areas under secondary and/or tertiary recovery), unexploited zones and unforeseen conditions likely to build due to uncertainties and heterogeneity in initially characterizing a reservoir. Whereas recompletion is prone due to sequential production of stacked reservoirs or multiple pay zones that is necessitated by regulation on comingling. The purpose of this paper is to optimize the time for sidetrack/recompletion job in multiple pay zones in view of maximizing returns on investment. A simple case to elicit the workflow is based on two pay zones and applies Experimental Design (ED), and economic analysis in the form of Net Present Value (NPV). The procedure followed for this study started with the identification of the reservoir uncertainties and their ranges; focusing basically on net pay thickness, porosity, permeability, and time to perform the sidetrack; the reservoirs are assumed to be undersaturated. The Box-Behnken response surface design in ED was used to reduce the number runs to make by generating the most effective combination of variables for the experiment. Experimental runs were conducted with a Black Oil reservoir simulator to give production profile. NPV values using stipulated Oil price, CAPEX, OPEX, tax rate, royalty and production rates were estimated, which was used afterwards to produce the Proxy model in ED and this developed correlation was used to analyse the effect of time changes. NPV computed from the Proxy Model was reasonable; however, a higher level D-Optimal design as against the 2-level design used in this study may be required for a reasonable match of NPV with respect to sidetrack (recompletion) time. In conclusion, the incorporation of time was successful and gives way to studying the impact of not only reservoir uncertainties but the uncertainty that arises from a success or failure of the sidetrack that further gives way to the application of decision analysis and the evaluation of NPV by Proxy Models for each possible outcome.

Development of proxy models for petroleum reservoir simulation: a systematic literature review and state-of-the-art

“…The authors [31] made the comparison of SRM with least square support vector machine. Use of experimental design to develop response surface [32][33][34][35][36][37][38][39][40][41], integrated with Monte Carlo simulations to characterise the response surface and to estimate the uncertainty [42,43]. Application of Bayesian multi-stage MCMC approach, based on an approximation with a linear expansion to reduce high computational costs [44], more accurately obtained model uncertainty and also assists in productionforecast business decisions [45], with Bayesian workflow based on two-step MCMC inversion [46].…”

“…The experiment of [39], with the ED application, involved many simulations and are made changes on the input variable. The authors [5] mentioned that, in an experiment, one or more variables could be changed to quantify the effect of inputs on outputs (response variables).…”