Eliminate Decision Bias in Facilities Planning

Abstract

Abstract The paper asserts that traditional Facilities planning methodologies, heavily based on Design Basis documents and biased towards the ‘most conservative conditions’, fail to recognize the entirety of operational conditions throughout the oilfield lifecycle, 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 the field life. A practical example is also provided. The proposed methodology employs Big Data algorithms to quantify subsurface and surface uncertainties and produce Probability Distribution Functions (PDF) for all relevant variables. Data sources are diverse, normally available in a project team, and may include geological and reservoir models and production forecasts, PVT reports, nodal and network analyses, and environmental databases. The relevant variables are expected to change throughout field life with different levels of uncertainty, due to planned and unplanned events such as condensate banking, lean gas injection for pressure maintenance, water breakthrough, etc. Stochastic algorithms (e.g. Monte Carlo method) are then applied on the deterministic model, randomly sampling from the previously defined probability distributions. Results are summarized as Tornado charts and spider or scatter plots, which are then used to analyze the most relevant variables and correlations affecting the objective function. The provided example results indicate the risk is still present in 9% of cases for the original, deterministic design. Furthermore, resources are wasted in 76% of cases. In the integrated stochastic design the risks and wastage are reduced down to a range of 0 to 1%. The associated OPEX components are reduced from USD 3 mln to USD 1 mln (-66%), expressed in Present Value terms. An unquantified increase in system availability is also noted. The paper demonstrates the utility of integrated, stochastic approach in Facilities planning, accounting for both subsurface and surface uncertainties and their interrelations throughout the field’s life. This approach eliminates the Design Basis induced bias and enables superior decisions at Project and Asset levels. The proposed approach is scalable, transferable to other oilfield challenges, and it is suited to multidisciplinary work environments.