Development of A Neural Boosted Model and JSL Code to Identify “Clean” or “Not Clean” Wells - A West Texas Sperry and Oklahoma Woodford Fractured Wells Coiled Tubing Cleaning Case Study

Authors: Trabelsi H*, Liu N, Trabelsi R and Boukadi F

DOI: 10.23880/ppej-16000377

Volume 8, Issue 1

Abstract

In a previous study, wellbore cleaning coefficient (WCC) correlations for cleaned wellbores out of debris and bridge plug remnants were developed for three conventional coiled tubing sizes (2.375”, 2.625”, and 2.875”). The following key performance indicators (KPIs): (1) slick water density ( ) f ρ , (2) slick water viscosity ( ) f μ , (3) hydraulic diameter c t (d - d ) between casing inner diameter (dc) and coil tubing outer diameter (dt), (4) average annular velocity (v) and (5) cleaning pressure gradient ΔP across a measured depth (MD) were employed in the empirical models. The models addressed operational conditions under which fractured wells will be identified as whether “clean” or “not clean”. In this study, the database from 150 wells, in the Spraberry formation in West Texas, was used to develop a predictive model to identify status of cleaned fractured wells: whether “clean” or “not clean”? About 70% of the data (99 wells) was used for training and about 30% (51 wells) for validation. 14 wells from the liquids-rich shale Woodford formation (Oklahoma) were utilized for testing. Six predictive modeling tools were designed to validate the derived empirical correlations. These tools are (1) Fit Stepwise, (2) Neural Boosted, (3) Boosted Tree, (4) Decision Tree (Partition), (5) Generalized Regression Lasso, and K-Nearest Neighbors. In the predictive models, independent variables are the annular velocity (AV), the Reynolds’ Number (Re), the Euler’s Number (Eu), and the coiled tubing roughness to internal radius ratio (ε/D). The dependent variable is well status; “clean” or “not clean”. Jump Scripting Language (JSL) code was used to develop user-friendly software. The software would be utilized to identify the fractured wellbore status, whether “clean” or “not clean”. Operators would be able to use the code to identify working conditions for which completed fractured wells are “clean” out of fracturing debris and remnants of bridge plugs or “not clean”. Input parameters to the code are AV, Re, Eu, and ε/D.

Keywords: Well Cleaning Coefficient; Neural Networks; Validation; Spraberry formation; West Texas; Woodford formation; Code; JSL

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