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.

The well treatment program is an important part of the field development 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 in 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 using 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 data driven, deterministic model, one cannot calculate the confidence interval of the predicted response. The confidence in results is purely based on the 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 using the normal score transform (NST) and by dividing the large dataset into smaller groups using clustering. The approach presented in this paper analyzes a dataset 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.

The well treatment program is an important part of the field development plan, and certain variables, such as job pause time (JPT), can affect its efficiency. JPT is the time during which pumping is paused between subsequent treatments of a job. 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. 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 using case studies first using classical CART analysis, then using CART analysis with enhancement tools such as the normal score transform (NST), and then dividing the large dataset into smaller groups using clustering. Significant variables are found that affect the response variables, and predictor variables are ranked in order of their importance. Such information can be used to control predictor variables that cause high JPT. The results are outlined in an intuitive way, including categorical, continuous, and missing values. Because CART is a data driven, deterministic model, we cannot calculate the confidence interval of the predicted response. Confidence in the results is purely based on the historical values, and the accuracy of the result produced by a tree model depends on the quality of the recorded data measured in terms of volume, reliability, and consistency. The prediction capability of CART is enhanced by the use of NST and clustering techniques. The approach presented in this paper analyzes a dataset with limited information and high uncertainty and should lead to developing a method for generating proxy models to find future success indices (e.g., for drilling efficiency or production from a fracture). This could standardize stimulation and generate decision ‘best practices’ to save costs in field development and the optimization process.

No abstract

The Powder River Basin in Wyoming has been exploited for the production of oil and gas for several decades. The development of horizontal drilling combined with large volume multi-stage hydraulic fracturing has initiated a new era of oil and gas production. More than 900 horizontal wells have been drilled in the Upper Cretaceous reservoirs of the Powder River Basin. Oil and gas producers often target more than one formation within any given acreage position. The focus of development has been in the Frontier Formation, the Turner sandy member, the Shannon and Sussex sandstone members of the Cody Shale, and the Parkman sandstone member of the Mesaverde Formation, along with the Niobrara Formation as an oil resource play. This paper presents a historical review of the completion designs and production trends from horizontal wells completed in the Upper Cretaceous formations. With a focus on multistage completions, well performance indicators are used in conjunction with a spatial sampling algorithm to identify dominant production influences attributed to completion and stimulation design. Using public records, a database was created, cataloging a completion metrics, including lateral pay length, fracture stage spacing, mass and type of fracture proppant, volume and type of fracturing fluid, and current production records. Correlations between fracture treatment design and hydrocarbon production are provided for each formation. Production type curves, initial production rates, and effective decline rates are presented for each formation. Geospatial trends in hydrocarbon properties, such as gas-oil ratio (GOR) and oil gravity are provided. The catalog of data and correlations provides a useful reference for exploring or developing acreage within the basin. Optimizing completion programs can be fast-tracked through the analysis of preceding completions and early time production indicators.