Efficient Assessment of Reservoir Uncertainty Using Distance-Based Clustering: A Review

Kang, Byeong‐Cheol; Kim, Sung-Il; Jung, Hun Soon; Choe, Jonggeun; Lee, Kyungbook

doi:10.3390/en12101859

Cited by 19 publications

(3 citation statements)

References 70 publications

(200 reference statements)

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“…GBM is sensitive to outliers and this can make it perform poorly in productivity forecasting (Han, D., et al 2020). Kang et al (2019) discuss the use of distance-based clustering techniques to assess uncertainties in the shale reservoir. Distance-based clustering is defined using distance, dimensional reduction, distance matrix construction, and clustering.…”

Section: Gradient Boosting Treementioning

confidence: 99%

AI/ML assisted shale gas production performance evaluation

Syed

Muther

Dahaghi

et al. 2021

J Petrol Explor Prod Technol

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Shale gas reservoirs are contributing a major role in overall hydrocarbon production, especially in the United States, and due to the intense development of such reservoirs, it is a must thing to learn the productive methods for modeling production and performance evaluation. Consequently, one of the most adopted techniques these days for the sake of production performance analysis is the utilization of artificial intelligence (AI) and machine learning (ML). Hydrocarbon exploration and production is a continuous process that brings a lot of data from sub-surface as well as from the surface facilities. Availability of such a huge data set that keeps on increasing over time enhances the computational capabilities and performance accuracy through AI and ML applications using a data-driven approach. The ML approach can be utilized through supervised and unsupervised methods in addition to artificial neural networks (ANN). Other ML approaches include random forest (RF), support vector machine (SVM), boosting technique, clustering methods, and artificial network-based architecture, etc. In this paper, a systematic literature review is presented focused on the AI and ML applications for the shale gas production performance evaluation and their modeling.

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Section: Gradient Boosting Treementioning

confidence: 99%

AI/ML assisted shale gas production performance evaluation

Syed

Muther

Dahaghi

et al. 2021

J Petrol Explor Prod Technol

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show abstract

“…Therefore, petroleum engineers use scenario reduction methods to choose a small subset of models (scenarios) that roughly reflect the features of the full ensemble, and these can also be applied in defining a production strategy. Consistent with this, two main groups of frameworks were introduced in the literature to reduce the number of scenarios: (1) scenario reduction using static features, (2) scenario reduction using simulation-based (dynamic) features (Kang et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Scenario reduction methodologies under uncertainties for reservoir development purposes: distance-based clustering and metaheuristic algorithm

Mahjour

Santos

Correia

et al. 2021

J Petrol Explor Prod Technol

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The simulation process under uncertainty needs numerous reservoir models that can be very time-consuming. Hence, selecting representative models (RMs) that show the uncertainty space of the full ensemble is required. In this work, we compare two scenario reduction techniques: (1) Distance-based Clustering with Simple Matching Coefficient (DCSMC) applied before the simulation process using reservoir static data, and (2) metaheuristic algorithm (RMFinder technique) applied after the simulation process using reservoir dynamic data. We use these two methods as samples to investigate the effect of static and dynamic data usage on the accuracy and rate of the scenario reduction process focusing field development purposes. In this work, a synthetic benchmark case named UNISIM-II-D considering the flow unit modelling is used. The results showed both scenario reduction methods are reliable in selecting the RMs from a specific production strategy. However, the obtained RMs from a defined strategy using the DCSMC method can be applied to other strategies preserving the representativeness of the models, while the role of the strategy types to select the RMs using the metaheuristic method is substantial so that each strategy has its own set of RMs. Due to the field development workflow in which the metaheuristic algorithm is used, the number of required flow simulation models and the computational time are greater than the workflow in which the DCSMC method is applied. Hence, it can be concluded that static reservoir data usage on the scenario reduction process can be more reliable during the field development phase.

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“…The concept of the TI rejection is to exclude the unsuitable TI, which has a large error compared to the observed production history after implementing reservoir simulation using static models from multiple TI candidates [6,8,9]. However, it requires complex procedures, e.g., distancebased clustering which is sensitive to the number of clusters and the definition of distance [10]. The concept of the blind well test is to quantitatively measure the degree of restoration of logging data that is excluded when MPS is executed from various TIs [11].…”

Section: Introductionmentioning

confidence: 99%

Managing Uncertainty in Geological Scenarios Using Machine Learning-Based Classification Model on Production Data

Kang

Lee

2020

Geofluids

Self Cite

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Training image (TI) has a great influence on reservoir modeling as a spatial correlation in the multipoint geostatistics. Unlike the variogram of the two-point geostatistics that is mathematically defined, there is a high degree of geological uncertainty to determine a proper TI. The goal of this study is to develop a classification model for determining the proper geological scenario among plausible TIs by using machine learning methods: (a) support vector machine (SVM), (b) artificial neural network (ANN), and (c) convolutional neural network (CNN). After simulated production data are used to train the classification model, the most possible TI can be selected when the observed production responses are put into the trained model. This study, as far as we know, is the first application of CNN in which production history data are composed as a matrix form for use as an input image. The training data are set to cover various production trends to make the machine learning models more reliable. Therefore, a total of 800 channelized reservoirs were generated from four TIs, which have different channel directions to consider geological uncertainty. We divided them into training, validation, and test sets of 576, 144, and 80, respectively. The input layer comprised 800 production data, i.e., oil production rates and water cuts for eight production wells over 50 time steps, and the output layer consisted of a probability vector for each TI. The SVM and CNN models reasonably reduced the uncertainty in modeling the facies distribution based on the reliable probability for each TI. Even though the ANN and CNN had roughly the same number of parameters, the CNN outperformed the ANN in terms of both validation and test sets. The CNN successfully classified the reference model’s TI with about 95% probability. This is because the CNN can grasp the overall trend of production history. The probabilities of TI from the SVM and CNN were applied to regenerate more reliable reservoir models using the concept of TI rejection and reduced the uncertainty in the geological scenario successfully.

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Efficient Assessment of Reservoir Uncertainty Using Distance-Based Clustering: A Review

Cited by 19 publications

References 70 publications

AI/ML assisted shale gas production performance evaluation

AI/ML assisted shale gas production performance evaluation

Scenario reduction methodologies under uncertainties for reservoir development purposes: distance-based clustering and metaheuristic algorithm

Managing Uncertainty in Geological Scenarios Using Machine Learning-Based Classification Model on Production Data

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