Automatic Well Log Analysis Across Priobskoe Field Using Machine Learning Methods
- Boris Belozerov (Science and Technology Center of Gazprom Neft) | Nikita Bukhanov (Science and Technology Center of Gazprom Neft) | Dmitry Egorov (Science and Technology Center of Gazprom Neft) | Adel Zakirov (Science and Technology Center of Gazprom Neft) | Oksana Osmonalieva (Science and Technology Center of Gazprom Neft) | Maria Golitsyna (IBM Science and Technology Center) | Alexander Reshytko (IBM Science and Technology Center) | Artyom Semenikhin (IBM Science and Technology Center) | Evgeny Shindin (IBM Haifa Research Lab) | Vladimir Lipets (IBM Haifa Research Lab)
- Document ID
- Society of Petroleum Engineers
- SPE Russian Petroleum Technology Conference, 15-17 October, Moscow, Russia
- Publication Date
- Document Type
- Conference Paper
- 2018. Society of Petroleum Engineers
- 7.6.6 Artificial Intelligence, 5 Reservoir Desciption & Dynamics, 5.6.1 Open hole/cased hole log analysis, 7 Management and Information, 6.1 HSSE & Social Responsibility Management, 5.6 Formation Evaluation & Management, 6 Health, Safety, Security, Environment and Social Responsibility, 7.6 Information Management and Systems, 7.6.7 Neural Networks, 6.1.5 Human Resources, Competence and Training
- 17 in the last 30 days
- 93 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 8.50|
|SPE Non-Member Price:||USD 25.00|
This paper is devoted to the testing of of automatic well logs interpretation testing, developed on the basis of machine learning methods.
The basis of the method presented in the paper is recurrent artificial neural networks. For their training and adjustment, log curves and their corresponding interpretation of different years are used. The set of well data is divided into training, validation and test samples. The resulting tool is set up on training and validation samples, and then used on a test sample of wells for which the interpretatoin was hidden, in order to automatically predict net pays and compare the results with the interpretation performed by an expert petrophysicist. For the test sample, traditional machine learning metrics metrics and special geological were calculated to assess the quality of the algorithm.
During the work a number of experiments were carried out, in which the dependence of the forecast quality was estimated not only on the different architecture and settings of the artificial neural network, but also on the amount of input data. The iterative approach in the research allowed to determine the best parameters for the solution of the task. For each well of the test set, a forecast of reservoir intervals distribution is made. The resulting interpretation shows high accuracy, both in terms of different mathematical metrics, and the results of analysis and evaluation of the expert petrophysics. Also, during the experiments, an important conclusion was made about the generalizing ability of the proposed methodology. The use of several variants of interpretation of well log data, performed by different specialists at different times and on the basis of different petrophysical models, allows to generalize and use all the accumulated experience of well logs interpretation, thereby improving the quality of the forecast. The main conclusion of the study can be considered a statement about the efficient applicability of machine learning algorithms for automatic well logs interpretation.
|File Size||1 MB||Number of Pages||21|
Hochreiter Sepp; Schmidhuber Jürgen (1997). "Long short-term memory". Neural Computation. 9 (8):1735-1780. doi:10.1162/neco.19184.108.40.2065. PMID 9377276.
Lipton Zachary C., Berkowitz John, Elkan Charles, A Critical Review of Recurrent Neural Networks for Sequence Learning, p>https://arxiv.org/abs/1506.00019
Transfer Learning for Dataset Shift in Classification and Clustering Problems p>http://researcharchive.vuw.ac.nz/xmlui/handle/10063/6808
Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation p>https://arxiv.org/abs/1702.07841