Prediction of Uncertainty in Exploration of Unconventional Gas Reservoirs

Abstract

Once a new unconventional play is identified, companies would start acquiring sizable land holdings within the same play. Before acquiring, the companies would like to answer several questions: how much to pay for the bonus; what is the strategy to initiate the drilling program and how to determine the uncertainty related to EUR from these wells. Most companies would use the data from existing wells within the same play or similar play to estimate what type of production to be expected from the land to be acquired. Based on the results, they will determine the bonus as well as expected uncertainties. We approach this problem using a novel method. We use the analog EUR data to develop the spatial relationships for the analog field. By assuming log-normal distribution for EUR data, we build the equations for the EUR for the new area. Appropriate scaling considerations can be included in these equations. For example, the newly acquired area may have smaller thickness than the analog or would use longer horizontal wells. We generate a graph of p10, p50 and p90 EUR values for the initial drilling program, starting with the first well. Knowing the uncertainty in EUR values, the initial bonus, based on appropriate economic criteria, can be determined. Once the land is acquired, as the initial drilling program is initiated, the method will allow the company to compare the performance of actual well with the expected uncertainty range. Depending on the locations of the wells and spatial relationship, the uncertainty will change differently. By using the method, the company can evaluate if the average performance of the drilled wells is within the realm of acceptable uncertainties or should the model be changed based on the new data available. We validated the proposed method for a shale play in Oklahoma.

Identify the Key Uncertainties Which Influence Production Performance and Recovery by Using Global Analogs

“…Its applications cover a wide range of uncertainty evaluation, including: (1) recovery factor determination (Adrian et al, 2013;Kacamarczyk et al, 2014); (2) reserve estimation (Hodgin and Harrell, 2006;Sidle and Lee, 2010); (3) generation of a conceptual model for uncertainty evaluation of green fields. (Paola et al, 2013); (4) direct uncertainty prediction (Kelkar et al, 2010); and (5) production profile generation (Lu et al, 2012;and Saeedi et al, 2014).…”