Eliminate Decision Bias in Facilities Planning

Abstract

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 187283, “Eliminate Decision Bias in Facilities Planning,” by Z. Cristea, Stochastic Asset Management, and T. Cristea, Consultant, prepared for the 2017 SPE Annual Technical Conference and Exhibition, San Antonio, Texas, USA, 8–11 October. The paper has not been peer reviewed. The complete paper holds the traditional facilities-planning methodologies, heavily based on design-basis documents and biased toward the most-conservative conditions, fail to recognize the entirety of operational conditions throughout the oilfield life cycle, leading to significant residual risk and the wastage of resources in the operations stage. An integrated stochastic approach is proposed, accounting for both subsurface and surface uncertainties and their interrelations throughout field life. Introduction The authors discuss an unbiased, data-driven stochastic work flow addressing the effect of subsurface uncertainties on surface-facilities design and operational decisions. Unlike classical design approaches, in which the most-conservative values are typically used as design input variables and assembled into design-basis documents, the stochastic work flow accounts for design-input-variable distribution and combination throughout the entire system life cycle. An example case is provided in which a flow-assurance risk is managed and chemical consumption optimized in a wet-gas field development. Theory and Definitions Oil and gas engineering projects are typically processes of high variety, low volume, and intermittent productivity, and with a high rate of diversification and complexity. Conversely, oilfield-facilities operations are expected to be continuous, characterized by high volumes and low variety. This expectation is reflected in the approach toward facilities design, where single-point, “conservative” design conditions are proposed and assembled as facilities design-basis documents. This approach frequently fails to recognize the risks and uncertainties associated with oilfield developments. In the proposed work flow, deterministic models are established to account for the dependencies between design input variables {static variables [i.e., bottomhole pressure (BHP) and bottomhole temperature (BHT)]} and the desired objective [static results (i.e., chemical- injection rate)]. In the provided example, the analyzed variables change because of subsurface and surface events with different levels of uncertainty (i.e., condensate banking, lean-gas injection, water breakthrough). Stochastic algorithms are used to create probability-distribution functions (PDFs) for all analyzed design input variables (stochastic variables). Stochastic algorithms are then applied in the deterministic model, sampling from the previously defined probability distributions. Stochastic results are assembled into insightful charts and used to analyze the most-relevant variables and correlations affecting the objective function.