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.
As the demand for energy increases, more and more low-permeability reservoirs are being developed with the help of advancements in hydraulic fracturing and horizontal drilling technologies. Integrated reservoir modeling studies become increasingly important to understand and improve reservoir management of such reservoirs for optimum depletion planning. This paper presents the approach used for applying an integrated reservoir modeling workflow to the tight gas sands of the Cotton Valley reservoir. The main objective of the study is to understand well performance for horizontal infill wells with multiple hydraulic fractures. In order to accurately simulate gas flow in hydraulically fractured wells (HFW) in full-field or regional models of unconventional gas reservoirs with many HFW, it is critical to appropriately represent these wells in the simulation models. Various methods have been used in the industry to numerically simulate hydraulic fractures in large reservoir models. In this paper, we will also review these methods and show that almost all the methods require calibration with fine grid models in which the hydraulic fracture is explicitly gridded. For homogeneous models calibration is relatively easy, but it is almost impossible for heterogeneous models. Therefore, we used local grid refinement (LGR) as the solution for modeling hydraulically fractured wells in coarse grid simulation. Our approach is different than other LGR approaches presented in the literature in that we only refine the coarse gridblocks that contain the fractures and wells. Finally, since it is very time consuming to generate LGRs manually for models with many wells a software tool was developed to generate LGR gridding automatically for HFW for commercial simulators.
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