Application of Representative Stochastic Models to Guide Development Decision Making in Large Unconventional Projects with Variable Fluid Composition

Abstract

Commercial development of large green field unconventional projects with concurrent late-stage appraisal and initial development drilling is fraught with challenges. While the resource may be laterally extensive, extracting the resource efficiently is an engineering challenge that involves decision making at multiple stages to sequence and stack segments so as to maximize value and minimize risk. In large unconventional developments, allocating capital for appraisal and development sequencing benefits from a structured stochastic approach addressing risks and uncertainties to remove subjectivity. This paper illustrates the application of stochastic methods to guide management decision making during initial project development. Large unconventional plays show spatial variability in fluid composition and productivity. A good appraisal program ensures sufficient wells are drilled, completed and tested to understand this variability. Learnings from such programs combined with learnings from analogue wells aid in categorizing PVT and well performance variability to identify priority areas of focus. Based on this analysis, representative models are determined for fluid windows incorporating a range of expected well deliverability. Using a structured stochastic approach, these representative models leverage a bespoke economic workflow integrating multiple uncertainties, constraints, costs and risks through a stochastic method. The workflow shown in this paper is based on synthetic data. Based on a range of outcomes, decision making metrics such as probability of commerciality, expected monetary value and peak funding exposure are calculated integrating value of information criteria. The key output from this integrated workflow enables prioritization of focus areas for appraisal and development including stacking and sequencing of resource segments to ensure effective capital allocation and supporting optimum development value. This paper highlights the benefit of applying a stochastic economic method to guide management decision making at the early stages of project development. The aim is to support management with an unbiased scientific approach which considers multiple uncertainties. This method helps prioritize appraisal and development options with multifaceted criteria for decision making and capital allocation.