Estimating ultimate recovery (EUR) in shale is a function of rock properties, well, and completion design parameters. The variation associated with these parameters are in source of uncertainty. In this paper the combined decline curve (CDC) approach is used to estimate the EUR of shale wells. CDC is a conservative approach that combines hyperbolic (in early time) and exponential (in later time) declines for production analysis. The major objective of this work is to condition the results of the CDC-EUR of shale wells to rock properties, well characteristics, and completion design parameters in a given shale asset. As the first step CDC-EUR is estimated. In the second step data-driven analytics using artificial neural networks is employed to condition the CDC-EUR to rock properties, well characteristics, and completion design parameters. Then, artificial Intelligence techniques are used in order to extract the nonlinear relationship between well productivity and reservoir characteristics and completion parameters. In this study 168 wells from Marcellus shale asset are examined. The major rock properties that are studied are porosity, total organic content, net thickness, and water saturation. Moreover, the effect of several design parameters, such as well inclination and azimuth, stimulated lateral length, stage length, number of clusters per stage, and amount of fluid and proppant are studied. A data driven model is developed using AI techniques. Results show that the model is able to find the relationship between the input parameters and EUR. Thus, this model will help petroleum professionals to have a better understanding of the effect of rock properties and design parameters on the future production from shale wells.
Estimating Ultimate Recovery for Shale Based on Facts Faegheh Javadi Natural gas, as one of the nation's major energy sources plays a vital role in the US energy mix. In recent years, the production from Shale has focused much attention on this source of hydrocarbon. As an essential step for the production planning, natural gas professionals estimate production and ultimate recovery (EUR) throughout the life of wells. The fluid production rate (q) usually varies as a function of rock properties, well, and completion design parameters. The variation associated with these parameters is a source of uncertainty in estimating the long term production for unconventional reservoirs. A number of methodologies have been suggested to estimate the long term production of shale wells. Decline curve analysis is the most widely used methodology in the estimation of the future production profile (Arps, 1945). However, its results have been determined to be over optimistic (Fanchi et al. 2013 and Dinh et al. 2014). Discrepancies between actual and estimated production values by Arps decline curves have been observed. This is dominant in low permeability reservoirs characterized by production over-estimation that is a consequence of large values of hyperbolic component (b-values higher than 1). A combination of Arps hyperbolic (in early time) and exponential decline (in later time) is employed to overcome this deficiency (production over estimation). This combination of Arps declines curves are referred to as Combined Decline Curves (CDC). The resulting estimation of EUR is quite conservative such that it provides lower EUR values than Power Law Exponential and the Stretched Exponential Decline Curve. The major objective of this research is to condition the results of the CDC-EUR of shale wells to rock properties, well characteristics, and completion design parameters in a given shale asset. The first step of this study is CDC-EUR estimation using Arps combined decline curves. In order to have a more accurate (conservative) estimation, the hyperbolic curve will be switched to exponential decline during later time in the well's life. Then, artificial intelligence will be employed to condition the CDC-EUR to rock properties, well characteristics, and completion design parameters. The major rock properties that will be studied in this research as input parameters include porosity, total organic carbon, net thickness, and water saturation. Moreover, the effect of several design parameters, such as well trajectories, completion, and hydraulic fracturing variables on CDC-EUR will be investigated. This model will help natural gas professionals to have a better understanding of the effect of rock properties and design parameters on future gas production of shale.
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