Scenario Analysis Tool for Probabilistic Analysis of Reserves

Abstract

Probabilistic assessment of reserves is commonly performed through a series of reservoir simulations over the range of field parameters. In most cases, an exhaustive study of parameter combinations is unfeasible because the number of parameters to be investigated is usually large. Statistical design of experiments (DOE) can be used to select a small number of reservoir simulation runs. The choice of design, especially its ability to capture complex interactions in field parameters, is crucial for DOE to be successful. To this end, we have developed a Scenario Analysis Tool (SAT) using DOE methodology for selecting scenarios to run in 3-D modelling and analyse the resulting reserves values to estimate the ultimate recovery. The primary goal of SAT is to minimise experimental effort (and cost) by recommending the minimum number of reservoir simulation runs required to accurately estimate uncertainty in hydrocarbon reserves for a given set of uncertainty parameters. The process has three main steps:specify the uncertainty framework to generate appropriate scenarios;model scenarios in 3-D simulators and input the response into SAT; anduse SAT to perform the analysis and generate the probabilistic response curve. SAT is implemented within the Microsoft Excel environment, making it user friendly with simple and intuitive data entry and minimal training requirements. The user is guided by menus and dialog boxes through a few simple sequential steps. SAT output includes reserves estimates, probabilistic response curves, tornado charts, and a comprehensive statistical output. Efficient designs results in a significant reduction in simulation effort for generating the reserves value. Usually, less than 0.1% of possible models need to be simulated and confidence levels are beyond exploration teams' expectations. The capability of SAT will be demonstrated through a number of examples. Introduction In reservoir uncertainty studies 3D geostatistical modelling is performed to quantify the distribution of the output response - for example, hydrocarbon-in-place volume and ultimate recovery. An important question to answer is: "Which scenarios should be selected for modelling?" Numerically, simulating a large number of 3D models is not practical, so it is important to select a small subset of models, making sure that we capture the significant effects due to key uncertainties. Using the statistical Design of Experiments (DOE) technique can significantly reduce the number of 3D models used to properly quantify the output response and its probability distribution curve. DOE has been successfully practiced since the 1930's in a broad number of fields, such as agriculture, biology, and chemistry. Using DOE methods in the oil and gas industry can be broadly classified into three categories:sensitivity studies;prediction; andoptimisation.

Gippsland Basin 3D forward modelling in Badlands

“…Experimental Design. This minimises the number of necessary scenarios from many thousands possible with the large number of variables, while ensuring that the full multi-dimensional space is statistically populated, and that all of the main effects and interactions are tested (Collinson et al, 2008). The main variables and some of the criteria are given in Table 1.…”

Experimental Design for Resource Estimation: A Comparison with the Probabilistic Method

“…Woodside's proprietary Scenario Analysis Tool (SAT) 6 was used to produce the experimental design matrix. SAT is capable of handling different levels of uncertainty and allows customisation of the two-way interaction terms that should be included in the design.…”

Probabilistic Aggregation of Oil and Gas Field Resource Estimates and Project Portfolio Analysis

“…Usually a mid-case is chosen, but tornado diagrams can also be constructed for other cases and these can in some instances be different (Collinson et al 2008). It is not unique for the field and is dependent on the choice of the reference case, because of interactions between the reservoir parameters.…”