An integrated approach to chemical EOR opportunity valuation : technical, economic, and risk considerations for project development scenarios and final decision



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Surfactant-polymer (SP) and alkali-surfactant-polymer (ASP) flooding has gained little traction among different tertiary recovery strategies such as thermal and miscible gas flooding; however, many mature onshore reservoirs could be potential candidates. More than four decades of research has detailed technical challenges and successes through laboratory experimentation, chemical flood simulation, and some pilot projects, which have provided technical screening procedures to efficiently filter unfeasible projects. Therefore, technical understanding seems sufficient to advance projects through early development stages; however, a project value identification and realization process ultimately dictates project implementation in the oil and gas industry, with technical feasibility merely supporting overall valuation and project feasibility. A quick early screening methodology integrating important project valuation criteria can efficiently assess large numbers of projects. The relatively few studies detailing chemical flooding valuation from just an economic standpoint reflects the need for an integrated process-oriented framework for quick early screening valuation of chemical flooding opportunities. This study develops an integrated process-oriented framework for early screening and valuation, with an overall objective to quickly filter unfeasible projects based on valuation criteria, rather than technical feasibility alone. A reservoir-to-market model was developed, integrating information from laboratory experiments (phase behavior, core flood), field analogues (well performance and layout), facilities, rigs, costs, scheduling, and economics. Recently published ASP flood data of the central Xing2 area in Daqing, China was used for model inputs. A reservoir-to-market benchmark model for a typical mature onshore field was successfully built and tested, and could value projects using standard economic metrics (net present value, internal rate of return, value investment ratio, unit technical cost, and payback period). Model simplification was achieved through global sensitivity analysis. Using a mean-reversion oil price model, the oil price accounted for 98% of the total sensitivity. . Model efficiency was achieved through discretization of input parameter uncertainties, which sped the screening process. Decision-making between model alternatives given information and different states of nature was performed through decision-tree techniques based on overall project valuation. Overall, this study was novel and provided benefit as a robust, integrated process-oriented framework for chemical EOR project screening, valuation, and decision-making.