Single Well Deconvolution – Multi-fractured Horizontal Shale Gas With 8 Well Full Pad – Canadian Horn River Basin Case Study

Abstract

Abstract Present trends towards using high frequency and big data such as SCADA allow the acquisition of large set of well data ranging over much longer periods of time than previously imaginable such as total production history. Such production and pressure data can contain information about the reservoir at a substantially larger radius of investigation than that accessible to unconventional derivative analysis, which is limited to the interpretation of single flow periods at constant rate. By contrast, deconvolution methods, do not suffer from this constraint as they are designed to perform well production and pressure data analysis at variable flow rate. In this work, we present a deconvolution method applied to multi-fractured horizontal shale gas wells. This work offers a new solution method to the long-standing deconvolution problem and makes deconvolution a viable tool for well-test and production and pressure data analysis. The following objective is proposed for this work: To apply this method to traditional variable-rate/pressure problems, such as long-term production data, high SCADA frequency pressure gauge data and well tests having multiple wells producing. This paper will show the deconvolution method based on the work of Von Schroeter et al and Michael Levitan et al. Deconvolution is a mathematical tool that extracts the drawdown type curve from the rate and pressure history. Essentially, the deconvolution process consists of the following steps: Generate a typecurve as an initial guess Superimpose this typecurve with historical rate data to calculate synthetic pressures Calculate the error between the calculated pressures and the measured pressures Generate a new typecurve and repeat the process until the error between calculated and measured pressures is minimized Deconvolution techniques provide an alternative to unconventional diagnostic analysis, and can show additional flow regime information that would not normally be seen within the specified time-frame of the buildup test. The deconvolved typecurve can then be analyzed for each multi-fractured shale gas horizontal well using unconventional diagnostic analysis techniques to determine various reservoir characteristics such as Stimulated Reservoir Volume (SRV) fracture half length, permeability, skin, reservoir size, effective wellbore length, effective stimulated stages, and more. We validate our work with examples using daily and minute data. Upon validation, we then demonstrate the deconvolution method using a variety of field cases, including traditional well tests, turnaround wellhead gauge data as well as production data of multi-fractured horizontal wells. Our work suggests that the deconvolution method has broad applicability in variable rate/pressure problems and can be implemented in typical well test and production data analysis applications. This study highlights not only the need of combining different frequency and quality controlled data but also pressure and rate type of data as well to get a full understanding of various reservoir characteristics within the SRV.