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Robustness Of Extra Net Thickness Identification Within Vertical And Spatial Scale Using Statistical Learning MethodsNormal access

Authors: A. Reshitko, M. Golytsina, A. Gruzdev, A. Semenikhin, D. Egorov, N. Bukhanov, O. Osmonalieva and B. Belozerov
Event name: ECMOR XVI - 16th European Conference on the Mathematics of Oil Recovery
Session: ECMOR XVI Poster Session 2
Publication date: 03 September 2018
DOI: 10.3997/2214-4609.201802178
Organisations: EAGE
Language: English
Info: Extended abstract, PDF ( 1.29Mb )
Price: € 20

Extra net thickness may bring a huge impact on projects NPV, especially in case of brownfields with vast production wells stock and maintained surface infrastructure. Reservoir beds with sand may be misinterpreted by petrophysicist within a well and miscorrelated spatially. We propose statistical learning methods to identify missed reservoir beds and therefore extra net thickness by predictions of supervised model. Robustness analysis of such identification is the main purpose of our paper. Methodology is tested on 3 brownfields in Western Siberia along with computational experiments with digital outcrop model, representing complex fluvial facies sedimentology. All the three brownfields represent different geological environment and have significant production history. Digital outcrop model is used primarily as a benchmark for different statistical learning algorithms. The main idea behind extra net thickness identification within vertical scale is to train the model on manual interpretation (reservoir/non-reservoir, binary classification) and perform predictions on validation wells. False positives errors give potential reservoir intervals, which were not identified in manual interpretation. Such candidates are evaluated by an expert and validated on production data through perforation. Recurrent neural network is chosen as the baseline algorithm for the methodology. The choice was made according to benchmark testing of different approaches (including Bayesian networks, support vector machines and others) and according to sensitivity analysis of training error for different size of training set (amount of wells). Although RNN gives high accuracy of prediction, this approach still need improvements in term of interpretability and generalization for brownfields covering regions with high variation of geological properties. Feature engineering includes augmentation and creating synthetic curves in case of absence of some significant well log. Missing or noisy well logs were reconstructed based on logs not only from a particular well but also on logs from its neighbor wells. Using of data from neighbor wells as additional features showed dramatic improvement of synthetic log quality. Robustness of a spatial forecast examined in the presented paper was dependent on a number of neighbor wells taken as features and search window size within a particular well. Evaluation of forecast accuracy was done not only by statistical but also by geological metrics such as compartmentalization and net-to-gross ratio. According to the experiments presented in this paper the optimal vertical window is around 1 meter thick, collected from 5 neighbor wells.

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