Analysis of Data From the Barnett Shale Using Conventional Statistical and Virtual Intelligence Techniques
- Obadare Awoleke (Texas A&M University) | Robert Lane (Texas A&M University)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Evaluation & Engineering
- Publication Date
- October 2011
- Document Type
- Journal Paper
- 544 - 556
- 2011. Society of Petroleum Engineers
- 1.6 Drilling Operations, 3.2.3 Hydraulic Fracturing Design, Implementation and Optimisation, 5.8.2 Shale Gas, 5.7.2 Recovery Factors
- Water loading, Analysis, Water production, Shale gas, Competitive learning network
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- 1,246 since 2007
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A Barnett shale water-production data set 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. 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, and a feed-forward neural network was used to predict average water production for wells drilled in Denton and Parker Counties, Texas, of the Barnett shale.
Using conventional techniques, we concluded 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 made 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 and 35% of the horizontal and vertical wells drilled, respectively, in Parker County have a load-recovery factor greater than unity.
The use of both self-organizing maps and the k-means algorithm showed that the data set 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 data set containing both vertical and horizontal wells. The average prediction error for the neural-network tool varied between 10 and 26%, depending on well type and location.
Results from this work can be used 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 method used to develop this tool can be used to solve similar challenges in new and existing shale plays.
|File Size||1 MB||Number of Pages||13|
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