Reservoir Ranking Map Sketching for Selection of Infill and Replacement Drilling Locations Using Machine Learning Technique
- Yuanjun Li (University of Southern California) | Robello Samuel (Haliburton)
- Document ID
- Society of Petroleum Engineers
- Abu Dhabi International Petroleum Exhibition & Conference, 12-15 November, Abu Dhabi, UAE
- Publication Date
- Document Type
- Conference Paper
- 2018. Society of Petroleum Engineers
- Ranking Map, Replacement Drilling, Infill Drilling, Machine Learning
- 64 in the last 30 days
- 64 since 2007
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Infill and replacement drilling are effective ways to improve oil recovery as increasingly more wells are drilled in close proximity for fracturing. Presently, the approaches being employed are logging surveys, the moving window method, the rapid inversion method, and the customized type curve method. However, these methods are not suitable for reservoirs with high levels of heterogeneity in terms of geology, and require more expert knowledge and field survey, which can be time consuming and costly. Therefore, the present method developed is an economic and fast approach to determine infill and replacement drilling location from reservoir ranking maps generated in combination with machine learning methods.
During this project, production data and reservoir parameters were gathered from an old oil field with more than 2,500 wells where most of the field was under water injection. Bubble maps were created for each reservoir parameter for a better visual representation of reservoir conditions. Then, after data cleansing and normalization procedures, the standout attributes were identified from all given reservoir parameters and production history and a reservoir ranking rule was set. Next, five types of classification approaches were used for prediction. This paper additionally presents a regression method, artificial neural network (ANN), to compare with the prediction results from classification. For each machine learning technique, a reserve ranking map was generated for this test field to predict future infill drilling and replacement drilling opportunities. Thus, with only geographic coordinates, the reserve ranking level was obtained.
From cross-fold validation results, a quadratic support vector machine provides the highest prediction accuracy. From a practical standpoint, a decision tree offers a more realistic result. In addition to the ANN method outputs, the ranking result provides a smooth method between certain levels. This new approach of using artificial intelligence was used to provide the ranking level and ranking number to identify the best options for drilling the wells, which is different from the present traditional methods. This advanced reservoir ranking map allows operators to identify the best location for infill or replacement drilling. It can additionally help operators benefit from their previously gathered knowledge in a cost-effective way.
|File Size||1 MB||Number of Pages||14|
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