Practical Data Mining: Analysis of Barnett Shale Production Results With Emphasis on Well Completion and Fracture Stimulation

LaFollette, Randy F.; Holcomb, William David; Aragon, Jorge

doi:10.2118/152531-ms

Cited by 49 publications

(6 citation statements)

References 2 publications

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“…Multiple studies demonstrate the high variability of return in virtually all shale plays with a sufficient number of wells drilled. Lafollette (Lafollette, 2012), (Randy F. LaFollette, 2012) and Dong (et al) (Z. Dong, 2012) provide examples of well performance variability. When translated into economic returns, similar results are obtained.…”

Section: Risks Associated With Shale Playsmentioning

confidence: 99%

A Comparison of North American and International Risks in Unconventional Resource Plays

Meehan

2014

All Days

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Unconventional resources are defined to include coal-bed methane (CBM), tight gas and source rock-(often shale) based reservoirs. Tight gas and CBM have not been "unconventional" in the United States and Canada for more than a decade and are being developed in many countries as more or less conventional plays. Source rock-based plays (shale oil, shale gas) in North America have become widely commercialized and contribute substantially to both gas and hydrocarbon liquid production. Vast quantities of shale resources outside of the United States and Canada have been identified but generally remain at early stages of development. Although large numbers of horizontal, multi-stage hydraulically fractured shale wells have been drilled commercially, such plays remain unconventional for a variety of reasons.Shale plays differ from tight gas, CBM and historically conventional plays for the same reasons. Unlike the other plays whose characteristics that result in success are well known, shale plays only have indicators pointing to success. The reasons for variability in individual well performance for shale plays are poorly known. Methodologies to "derisk" potential plays vary. Mathematical models to predict performance for shale plays are less rigorous and operators often rely on overly simplified approaches. Highly competitive North American markets with scores of well-funded competitors and relatively easy access to land and technology have resulted in rapid evaluation of many shale plays at the cost of a substantial number of underperforming wells. In the study, several plays are presented, showing how quickly derisking occurs, what factors contribute to them and how they impact long-term efficiency. A consistent method for gathering data (seismic, test wells, cores, logs, geomechanical information) is proposed to minimize excess costs and maximize NPV. The approaches to optimize development are applied to shale plays with synthetic properties established on observed heterogeneities but reservoir characteristics for some international shale prospects. The economics are tested with various strategies to illustrate the differences in value obtained.

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Section: Risks Associated With Shale Playsmentioning

confidence: 99%

A Comparison of North American and International Risks in Unconventional Resource Plays

Meehan

2014

All Days

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“…In recent years, several publications have dealt with the application of data mining/analytics for the assessment of unconventional resources [2,3,4,5]. These studies cover a broad range of techniques such as advanced non-parametric regression, tree-based modeling, classification tree analysis, fuzzy clustering, time-series analysis, etc.…”

Section: Introductionmentioning

confidence: 99%

Data Analytics for Production Optimization in Unconventional Reservoirs

Schuetter¹,

Mishra²,

Zhong³

et al. 2015

Unconventional Resources Technology Conference

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Considerable amounts of data are being generated during the development and operation of unconventional reservoirs. Statistical methods that can provide data-driven insights into production performance are gaining in popularity. Unfortunately, the application of advanced statistical algorithms remains somewhat of a mystery to petroleum engineers and geoscientists. The objective of this paper is to provide some clarity to this issue, focusing on: (a) how to build robust predictive models, and (b) how to develop decision rules that help identify factors separating good wells from poor performers. The data for this study come from wells completed in the Wolfcamp shale formation in the Permian Basin. Data categories used in the study included well location and assorted metrics capturing various aspects of well architecture, well completion, stimulation, and production.Predictive models for the production metric of interest are built using simple regression and other advanced methods such Random Forests, Support Vector Regression, Gradient Boosting Machine and Multidimensional Kriging. The data fitting process involves splitting the data into a training set and a test set, building a regression model on the training set and validating it with the test set. Repeated application of a "cross-validation" procedure yields valuable information regarding the robustness of each regression modeling approach. Furthermore, decision rules that can identify extreme behavior in production wells (i.e., top x% of the wells versus bottom x% as ranked by the production metric) are generated using the classification and regression tree algorithm. The resulting decision tree provides useful insights as to what variables (or combinations of variables) can drive production performance into such extreme categories.The main contributions of this paper are to provide guidelines on how to build robust predictive models, and to demonstrate the utility of decision trees for identifying factors responsible for good versus poor wells.

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“…CART has been applied in several areas, such as the financial industry (Cashin and Dattagupta 2008), manufacturing and marketing (Chen and Su 2008), and medical industries (Snousy et al 2011), and even in weed science (Wiles and Brodahl 2004). Different versions of decision trees have also been applied in the petroleum industry to estimate production profiles along with uncertainty assessments in long-term production forecasts (Jensen 1998); for data classification and partitioning to predict permeability from well logs (Perez et al 2005); for case-based reasoning and planning of the execution of a fracturing job (Popa and Wood 2011); to predict average production of a well from several variables, such as producer, acid volume, and strength (Yarus et al 2006); and to predict the oil production from five significant parameters (permeability, porosity, first shut-in pressure, residual oil, and water saturation) by use of a neural-based decision-tree model (Lee and Yen 2002). Recently, the boosted regression tree was applied to the data from more than 15,000 producing wells in the Barnett shale play to predict maximum gas rates and find the relative importance of the different inputs used in the treatment (Lafollette et al 2012).…”

Section: Introductionmentioning

confidence: 99%

Multivariate Analysis and Data Mining of Well-Stimulation Data by Use of Classification-and-Regression Tree With Enhanced Interpretation and Prediction Capabilities

Maučec

Singh

Bhattacharya

et al. 2015

SPE Economics &Amp; Management

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The well-treatment program is an important part of the fielddevelopment plan, and certain variables, such as job-pause time (JPT) and fracture screenout, can affect its efficiency. JPT is the time during which pumping is paused between subsequent treatments of a job. Screenout occurs because of a sudden restriction of fluid flow inside the fracture and through the perforation. The objectives of this work are to investigate whether, from existing data, it is possible to find patterns in significant variables that affect the extreme values of JPT in a particular region, and what is the most critical variable causing screenout. The answers are sought by applying a classification-and-regression tree (CART) to both categorical and continuous variables in the database. The practical application of CART is presented by use of case studies containing JPT and screenout. Significant variables are found that affect the response variables, and predictor variables are ranked in the hierarchal order of their importance. Such information can be used to control predictor variables that cause high JPT or screenout.The results are outlined in an intuitive way, including categorical, continuous, and missing values. Because CART is a datadriven, deterministic model, one cannot calculate the confidence interval of the predicted response. The confidence in results is purely because of historical values, and the accuracy of the result produced by a tree model depends on the quality of recorded data measured in terms of volume, reliability, and consistency. The prediction capability of CART is enhanced by use of the normalscore transform and by dividing the data set into smaller groups by use of clustering. The approach presented in this paper analyzes a data set under limited information and high uncertainty and should lead to developing methodology for generating proxy models to find future success indices (e.g., one for drilling efficiency or production from a fracture). This could standardize stimulation and generate decision practices to save costs in field development and the optimization process.

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Practical Data Mining: Analysis of Barnett Shale Production Results With Emphasis on Well Completion and Fracture Stimulation

Cited by 49 publications

References 2 publications

A Comparison of North American and International Risks in Unconventional Resource Plays

A Comparison of North American and International Risks in Unconventional Resource Plays

Data Analytics for Production Optimization in Unconventional Reservoirs

Multivariate Analysis and Data Mining of Well-Stimulation Data by Use of Classification-and-Regression Tree With Enhanced Interpretation and Prediction Capabilities

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