Zone Selection and Production Prediction Using Advanced Logging Technology

摘要

Completing wells with multiple pay sections can be difficultand costly. With limited capital allocated at completion time,the geologist and the completion engineer are faced withdeciding how to spend the available funds to attain the bestreturn on investment (ROI) or net present value (NPV).A process has been developed for selecting and rankingmultiple pay sections and for giving accurate post-stimulationproduction estimates with advanced logging technology, suchas magnetic resonance imaging (MRI), in conjunction with aproduction reservoir simulator.This process ranks multiple pay sections by hydrocarbonstorage capacity (Φeff)(h), and producing capacity (kh). Initialproductions (IP’s), Estimated Ultimate Recoveries (EUR’s), orprojected cumulative production is then predicted for eachsection with variable stimulation half-lengths (Xf) or skinvalues (S). Output can be displayed on the log or in a tabularformat.NPV, ROI, or the internal rate of return (IRR) can beestimated for each stimulation scenario. These estimations canhelp operators justify stimulation job size and completion costfor each section of pay selected, giving the operator anadditional tool for deciding how to best spend completiondollars.The ranking process can be performed rapidly, and it providesa producing potential before setting pipe. This paper illustratesthe process with six field examples from the Granite Washformation in the Ammunition Field of Washita County, OK.Actual production is compared to predicted production foreach well.