Analysis of Data from the Barnett Shale Using Conventional Statistical and Virtual Intelligence Techniques

摘要

A Barnett Shale water production dataset from approximately 11,000 completions was analyzed using conventional statistical techniques. Additionally a water-hydrocarbon ratio and first derivative diagnostic plot technique developed elsewhere for conventional reservoirs was extended to analyze Barnett Shale water production mechanisms. In order to determine hidden structure in well and production data, self-organizing maps and the k-means algorithm were used to identify clusters in data. A competitive learning based network was used to predict the potential for continuous water production from a new well for and a feed-forward neural network was used to predict average water production for wells drilled in Denton and Parker Counties of the Barnett Shale. Using conventional techniques, we conclude that for wells of the same completion type, location is more important than time of completion or hydraulic fracturing strategy. Liquid loading has potential to affect vertical more than horizontal wells. Different features were observed in the spreadsheet diagnostic plots for wells in the Barnett Shale; and we make a subjective interpretation of these features. We find that 15% of the horizontal and vertical wells drilled in Denton County have a load water recovery factor greater than unity. Also, 15% / 35% of the horizontal / vertical wells drilled in Parker County have a load recovery factor of greater than unity. The use of both self organizing maps and the k-means algorithm show that the dataset is divided into two main clusters. The physical properties of these clusters are unknown but interpreted to represent wells with high water throughput and those with low water throughput. Expected misclassification error for the competitive learning based tool was approximately 10% for a dataset containing both vertical and horizontal wells. The average prediction error for the neural network tool varied between 10-26%, depending on well type and location. Results from this work can be utilized to mitigate risk of water problems in new Barnett Shale wells and predict water issues in other shale plays. Engineers are provided a tool to predict potential for water production in new wells. The methodology used to develop this tool can be used to solve similar challenges in new and existing shale plays.