Data Conditioning and Forecasting Methodology using Machine Learning on Production Data for a Well Pad

2020 ◽  
Author(s):  
Maryam Bagheri ◽  
Haoran Zhao ◽  
Manyang Sun ◽  
Li Huang ◽  
Srinath Madasu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Production Data ◽  
Data Conditioning

Machine learning-assisted production data analysis in liquid-rich Duvernay Formation

2021 ◽  
Vol 200 ◽  
pp. 108377
Author(s):  
Bing Kong ◽  
Zhuoheng Chen ◽  
Shengnan Chen ◽  
Tianjie Qin
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Production Data

scholarly journals Ensemble Machine Learning Assisted Reservoir Characterization Using Field Production Data–An Offshore Field Case Study

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1052
Author(s):  
Baozhong Wang ◽  
Jyotsna Sharma ◽  
Jianhua Chen ◽  
Patricia Persaud
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Reservoir Characterization ◽  
Time Lapse ◽  
Production Data ◽  
Oil Saturation ◽  
Ensemble Machine Learning ◽  
Input Parameters ◽  
Saturation Profiles ◽  
Field Production

Estimation of fluid saturation is an important step in dynamic reservoir characterization. Machine learning techniques have been increasingly used in recent years for reservoir saturation prediction workflows. However, most of these studies require input parameters derived from cores, petrophysical logs, or seismic data, which may not always be readily available. Additionally, very few studies incorporate the production data, which is an important reflection of the dynamic reservoir properties and also typically the most frequently and reliably measured quantity throughout the life of a field. In this research, the random forest ensemble machine learning algorithm is implemented that uses the field-wide production and injection data (both measured at the surface) as the only input parameters to predict the time-lapse oil saturation profiles at well locations. The algorithm is optimized using feature selection based on feature importance score and Pearson correlation coefficient, in combination with geophysical domain-knowledge. The workflow is demonstrated using the actual field data from a structurally complex, heterogeneous, and heavily faulted offshore reservoir. The random forest model captures the trends from three and a half years of historical field production, injection, and simulated saturation data to predict future time-lapse oil saturation profiles at four deviated well locations with over 90% R-square, less than 6% Root Mean Square Error, and less than 7% Mean Absolute Percentage Error, in each case.

From data conditioning, depth imaging and reservoir characterization to machine learning

First Break ◽  
2020 ◽  
Vol 38 (6) ◽  
pp. 71-77
Author(s):  
P.C.H. Veeken ◽  
A. Kashubin ◽  
D. Curia ◽  
Y. Davydenko ◽  
I.I. Priezzhev
Keyword(s):  
Machine Learning ◽  
Reservoir Characterization ◽  
Depth Imaging ◽  
Data Conditioning

scholarly journals Time series analysis and forecasting of China’s energy production during Covid-19: statistical models vs machine learning models

2021 ◽  
Author(s):  
Zekai Lu ◽  
Nian Liu ◽  
Ying Xie ◽  
Junhui Xu
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Statistical Models ◽  
Energy Production ◽  
Production Data ◽  
Learning Models ◽  
Energy Research ◽  
Public Health Emergencies ◽  
Production Plans ◽  
Machine Learning Models

Abstract COVID-19 is a huge catastrophe of global proportions, and this catastrophe has had far-reaching effects on energy production worldwide. In this paper, we build traditional statistical models and machine learning models to forecast energy production series in the post-pandemic period based on Chinese energy production data and COVID-19 Chinese epidemic data from 2018 to 2021. The experimental results showed that the optimal models in this study outperformed the baseline models on each series, with MAPE values less than 10. Further studies found that the LightGBM, NNAT and LSTM machine learning models worked better in unstable energy series, while the ARIMA statistical model still had an advantage in stable energy time series. Overall, the machine learning models outperformed the traditional models during COVID-19 in terms of prediction. Our findings provide an important reference for energy research in public health emergencies, as well as a theoretical basis for factories to adjust their production plans and governments to adjust their energy decisions during COVID-19.

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

Geofluids ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-16 ◽  
Author(s):  
Byeongcheol Kang ◽  
Kyungbook Lee
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Great Influence ◽  
Classification Model ◽  
Support Vector ◽  
Production Data ◽  
Geological Uncertainty ◽  
Production History ◽  
Test Sets ◽  
Geological Scenario

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.

scholarly journals Conditioning well data to rule-based lobe model by machine learning with a generative adversarial network

2020 ◽  
Vol 38 (6) ◽  
pp. 2558-2578
Author(s):  
Honggeun Jo ◽  
Javier E Santos ◽  
Michael J Pyrcz
Keyword(s):  
Machine Learning ◽  
Deep Water ◽  
Image Inpainting ◽  
Local Data ◽  
Generative Adversarial Network ◽  
Rule Based ◽  
Adversarial Network ◽  
Stacking Pattern ◽  
Data Conditioning ◽  
Well Data

Rule-based reservoir modeling methods integrate geological depositional process concepts to generate reservoir models that capture realistic geologic features for improved subsurface predictions and uncertainty models to support development decision making. However, the robust and direct conditioning of these models to subsurface data, such as well logs, core descriptions, and seismic inversions and interpretations, remains as an obstacle for the broad application as a standard subsurface modeling technology. We implement a machine learning-based method for fast and flexible data conditioning of rule-based models. This study builds on a rule-based modeling method for deep-water lobe reservoirs. The model has three geological inputs: (1) the depositional element geometry, (2) the compositional exponent for element stacking pattern, and (3) the distribution of petrophysical properties with hierarchical trends conformable to the surfaces. A deep learning-based workflow is proposed for robust and non-iterative data conditioning. First, a generative adversarial network learns salient geometric features from the ensemble of the training rule-based models. Then, a new rule-based model is generated and a mask is applied to remove the model near local data along the well trajectories. Last, semantic image inpainting restores the mask with the optimum generative adversarial network realization that is consistent with both local data and the surrounding model. For the deep-water lobe example, the generative adversarial network learns the primary geological spatial features to generate reservoir realizations that reproduce hierarchical trend as well as the surface geometries and stacking pattern. Moreover, the trained generative adversarial network explores the latent reservoir manifold and identifies the ensemble of models to represent an uncertainty model. Semantic image inpainting determines the optimum replacement for the near-data mask that is consistent with the local data and the rest of the model. This work results in subsurface models that accurately reproduce reservoir heterogeneity, continuity, and spatial distribution of petrophysical parameters while honoring the local well data constraints.

scholarly journals Predicting the Surveillance Data in a Low-Permeability Carbonate Reservoir with the Machine-Learning Tree Boosting Method and the Time-Segmented Feature Extraction

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6307
Author(s):  
Cong Wang ◽  
Lisha Zhao ◽  
Shuhong Wu ◽  
Xinmin Song
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Feature Extraction ◽  
Multivariate Time Series ◽  
Oil Production ◽  
Carbonate Reservoir ◽  
Surveillance Data ◽  
Production Data ◽  
Low Permeability ◽  
Boosting Method

Predictive analysis of the reservoir surveillance data is crucial for the high-efficiency management of oil and gas reservoirs. Here we introduce a new approach to reservoir surveillance that uses the machine learning tree boosting method to forecast production data. In this method, the prediction target is the decline rate of oil production at a given time for one well in the low-permeability carbonate reservoir. The input data to train the model includes reservoir production data (e.g., oil rate, water cut, gas oil ratio (GOR)) and reservoir operation data (e.g., history of choke size and shut-down activity) of 91 producers in this reservoir for the last 20 years. The tree boosting algorithm aims to quantitatively uncover the complicated hidden patterns between the target prediction parameter and other monitored data of a high variety, through state-of-the-art automatic classification and multiple linear regression algorithms. We also introduce a segmentation technique that divides the multivariate time-series production and operation data into a sequence of discrete segments. This feature extraction technique can transfer key features, based on expert knowledge derived from the in-reservoir surveillance, into a data form that is suitable for the machine learning algorithm. Compared with traditional methods, the approach proposed in this article can handle surveillance data in a multivariate time-series form with different strengths of internal correlation. It also provides capabilities for data obtained in multiple wells, measured from multiple sources, as well as of multiple attributes. Our application results indicate that this approach is quite promising in capturing the complicated patterns between the target variable and several other explanatory variables, and thus in predicting the daily oil production rate.

Machine learning powered software for accurate prediction of biogas production: A case study on industrial-scale Chinese production data

2019 ◽  
Vol 218 ◽  
pp. 390-399 ◽  
Author(s):  
Djavan De Clercq ◽  
Devansh Jalota ◽  
Ruoxi Shang ◽  
Kunyi Ni ◽  
Zhuxin Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Biogas Production ◽  
Accurate Prediction ◽  
Industrial Scale ◽  
Production Data

scholarly journals A machine learning framework for rapid forecasting and history matching in unconventional reservoirs

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shriram Srinivasan ◽  
Daniel O’Malley ◽  
Maruti K. Mudunuru ◽  
Matthew R. Sweeney ◽  
Jeffrey D. Hyman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Transfer Learning ◽  
History Matching ◽  
Time History ◽  
Unconventional Reservoirs ◽  
Production Data ◽  
High Fidelity ◽  
Reduced Order Models ◽  
Reduced Order

AbstractWe present a novel workflow for forecasting production in unconventional reservoirs using reduced-order models and machine-learning. Our physics-informed machine-learning workflow addresses the challenges to real-time reservoir management in unconventionals, namely the lack of data (i.e., the time-frame for which the wells have been producing), and the significant computational expense of high-fidelity modeling. We do this by applying the machine-learning paradigm of transfer learning, where we combine fast, but less accurate reduced-order models with slow, but accurate high-fidelity models. We use the Patzek model (Proc Natl Acad Sci 11:19731–19736, 10.1073/pnas.1313380110, 2013) as the reduced-order model to generate synthetic production data and supplement this data with synthetic production data obtained from high-fidelity discrete fracture network simulations of the site of interest. Our results demonstrate that training with low-fidelity models is not sufficient for accurate forecasting, but transfer learning is able to augment the knowledge and perform well once trained with the small set of results from the high-fidelity model. Such a physics-informed machine-learning (PIML) workflow, grounded in physics, is a viable candidate for real-time history matching and production forecasting in a fractured shale gas reservoir.

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