Abstract
This paper presents an unbiased Stochastic Workflow (SW), data driven, where surface and subsurface uncertainties are accounted for and their impact on Facilities design and operational decisions quantified. Unlike the traditional approach in Facilities design where typically the ‘most conservative values' are used as design input variables, the proposed workflow accounts for lifecycle variability and correlations of relevant input data. The workflow enables superior risk management and resources allocation. An example is provided, where the traditional Facilities design outcomes are compared with the Stochastic Workflow findings.
Deterministic Models are established to account for the dependencies between design input variables (Static Variables, i.e. bottom hole pressure and temperature) and the desired objective (Static Results, i.e. chemical injection rate). However, in real life situations, the analyzed variables change due to 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 (PDF) for all analyzed design input variables (Stochastic Variables). Stochastic Algorithms are then applied on 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 their correlations affecting the model objectives.
The workflow provides an objective quantification of risks and uncertainties impacting the design and operation of the analyzed system. The deterministic design approach in the example permits for risk to be still present in 11% of cases and resources to be wasted in 77% of cases. In the revised design, based on the Stochastic Workflow, the risk and wastage are reduced to less than 1%. The associated OPEX component is reduced from USD 12 mln to USD 8 mln (-33%), expressed in Present Value terms.
This paper contributes to the efforts of bridging the gap between subsurface and surface disciplines, and demonstrates the utility of integrated approach in Facilities planning, where both subsurface and surface uncertainties are accounted for. This approach contributes to the elimination of subjective decision biases (Waring, 2017), enabling superior Project and Asset Management. The proposed Stochastic Workflow is scalable and transferable, and suited to collaborative, multidisciplinary project and asset teams.
Additional benefits of the Stochastic Workflow, such as improved Well and Reservoir Management (Virtual PLT) or increased system availability, are also mentioned in the paper.
