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In unconventional reservoirs, not only do we have to concern ourselves with having a sufficient hydrocarbon volume to justify development (Resource Fairway), but also whether producibility characteristics, especially the ability to effectively fracture stimulate, are favorable (commercially). While volumetric characteristics tend to be regionally variable resulting in changes in hydrocarbon fluid types, reservoir pressures, thickness, porosity, saturation, etc., at the basin scale, producibility characteristics tend to be more locally variable resulting in changes in natural fracturing, subsurface stresses, permeability, effectiveness of fracture stimulation barriers, etc., at the field scale. A stochastic approach encompasses both these trend variations (regional and local) while assessing the commerciality of the play. This paper will explain the workflow involved in creating a probabilistic model using a commercial tool for engineers to understand the mechanics of the model, quality-check the input distributions, compare the model results to those from other sources (such as empirical, analytical, or numerical models). This workflow also will help in seamlessly modifying the input parameters to generate "what-if" scenarios and stress test the base case of development projects. Due to the capital-intensive nature of Unconventional projects, it is important to economically model a series of staged-investments. The purpose of the staging is to responsibly expose incremental capital and identify course corrections. Several factors and combination of inputs affect the commerciality of unconventional developments. These inputs will then be sampled using Monte Carlo simulation to generate a multitude of trials and their summary statistics. The recoverable resources, costs, and economics can also be exported for any given trial, and the ranges of input parameters sampled to generate a given trial can also be extracted Five critical parameters and their ranges were identified for developing a stochastic Before Tax (BTAX) economic output reflecting the full range of outcomes. Well type curves and select components of Capex and Opex were identified as the primary drivers. Associated products, such as condensate, NGLs, ethane, sulfur and water, were modeled as a function of the primary product using dependent ratios. Variability in cycle time was added to account for delays in implementation and acceleration as learnings are applied. This paper will present how technological integration between legacy in-house developed tools and stochastic analysis engines, as the centerpiece, can provide management the required level of visibility on project economics and evaluation ranges. With the considerable time spared in carrying out economic assessments due to this workflow, engineers are able to spend more time in developing case scenarios, run sensitivities, and analyze outcomes and results to make informed portfolio management decisions. |
