Machine learning assisted history matching for a deepwater lobe system

Jo, Honggeun; Pan, Wen; Santos, Javier E.; Jung, Hun Soon; Pyrcz, Michael J.

doi:10.1016/j.petrol.2021.109086

Cited by 24 publications

(4 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results showed that the CNN-PCA method achieved satisfactory HM and uncertainty reduction for existing wells as well as reasonable predictions for new wells. Finally, Jo et al [159] developed GAN models to match a deepwater lobe system, trained by applying rule-based models to explore the latent reservoir space, since that way the multi-dimensional data that are used are converted into latent random vectors. The models produced are integrated with a simulator to generate production values and then an Ensemble Kalman Filter (EnKF) updates the latent vectors by minimizing the error obtained when comparing the calculated production values with the real ones.…”

Section: History Matching ML Methods Using Dimensionality Reduction T...mentioning

confidence: 99%

“…ANNs with stochastic optimization [106,107] ANNs with dimensionality reduction methods [108] Ensembles of ANNs [109] RBFNNs, Generalized Regression ANNs, FSSC and ANFIS stochastic optimization [110][111][112] Direct history matching Supervised ANNs [114][115][116][117][118][119][120]126] Bayesian ML models [121,122,124,125] MARS, DTs, single-pass GRNNs [127] Unsupervised Self-Organizing Map (SOM) [131] Supervised SVR with dimensionality reduction and optimization [135] RNN [142,143] CNN [148,158] Unsupervised GAN [149,159] Piecewise Reconstruction from a Dictionary (PRaD) with pluri-PCA [151] Convolutional AutoEncoders [152,157] Reinforcement learning Reinforcement learning models [103,162] Currently, dozens of professional products used to set up ML models are available to developers, might that be related to research or commercial products. This palette includes client tools developed by major players in the market such as Google and Microsoft (Google cloud AI platform, Azure machine learning) as well as free tools such as TensorFlow by Google and the Anaconda distribution for Python.…”

Section: Indirect History Matching Supervisedmentioning

confidence: 99%

See 1 more Smart Citation

Applications of Machine Learning in Subsurface Reservoir Simulation—A Review—Part I

Samnioti,

Gaganis

2023

Energies

View full text Add to dashboard Cite

In recent years, machine learning (ML) has become a buzzword in the petroleum industry with numerous applications that guide engineers toward better decision making. The most powerful tool that most production development decisions rely on is reservoir simulation with applications in numerous modeling procedures, such as individual simulation runs, history matching and production forecast and optimization. However, all these applications lead to considerable computational time- and resource-associated costs, and rendering reservoir simulators is not fast or robust, thus introducing the need for more time-efficient and smart tools like ML models which can adapt and provide fast and competent results that mimic simulators’ performance within an acceptable error margin. The first part of the present study (Part I) offers a detailed review of ML techniques in the petroleum industry, specifically in subsurface reservoir simulation, for cases of individual simulation runs and history matching, whereas ML-based production forecast and optimization applications are presented in Part II. This review can assist engineers as a complete source for applied ML techniques since, with the generation of large-scale data in everyday activities, ML is becoming a necessity for future and more efficient applications.

show abstract

Section: History Matching ML Methods Using Dimensionality Reduction T...mentioning

confidence: 99%

Section: Indirect History Matching Supervisedmentioning

confidence: 99%

Applications of Machine Learning in Subsurface Reservoir Simulation—A Review—Part I

Samnioti,

Gaganis

2023

Energies

View full text Add to dashboard Cite

show abstract

“…In the last few years, more and more efficient and accurate DA‐based methods for subsurface structure identification have been proposed by improving one or more of the modules mentioned above (Jardani et al., 2022; Jo et al., 2021; Kang et al., 2021; Mo et al., 2020; Sebacher & Toma, 2022). Within the context of reducing first arrival time uncertainty, Rizzo and de Barros (2019) proposed an iterative sampling method that relies on a graph theory‐based connectivity metric to capture preferential flow paths.…”

Section: Introductionmentioning

confidence: 99%

Data‐Worth Analysis for Heterogeneous Subsurface Structure Identification With a Stochastic Deep Learning Framework

Zhan

Dai

Soltanian

et al. 2022

Water Resources Research

View full text Add to dashboard Cite

show abstract

“…Petrophysical interpretation is important for accurate reservoir characterization, reservoir modeling calibration and decision making during the reservoir development (Xu et al, 1992;Pan et al,2021;Santos et al, 2021;Jo et al, 2021). However, the petrophysical interpretation of complex carbonate reservoirs, such as the prediction of formation properties, determination of geological settings and well-to-well correlation, is a major challenge in the oil and gas industry (Greder et al, 1996;Mohaghegh et al, 1997).…”

Section: Introductionmentioning

confidence: 99%

Reducing the Uncertainty of Multi-Well Petrophysical Interpretation from Well Logs via Machine-Learning and Statistical Models

Pan¹,

Torres‐Verdín²,

Duncan³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Well-log interpretation provides in situ estimates of formation properties such as porosity, hydrocarbon pore volume, and permeability. Reservoir models based on well-log-derived formation properties deliver reserve-volume estimates, production forecasts, and help with decision making in reservoir development. However, due to measurement errors, variability of well logs due to multiple measurement vendors, different borehole tools, and non-uniform drilling/borehole conditions, conventional well-log interpretation methods may not yield accurate estimates of formation properties, especially in the context of multi-well interpretation. To improve the robustness of multi-well petrophysical interpretation, well-log normalization techniques such as two-point scaling and mean-variance normalization are commonly used to impose stationarity constraints for well logs requiring correction. However, these techniques are mostly based on the marginal distribution of well logs and require expert knowledge to be effectively implemented. To reduce the uncertainties and time associated with multi-well petrophysical interpretation, we develop the discriminative adversarial (DA) model and the linear constraint model for well-log normalization and interpretation. We also develop a new divergence-based type well identification method for improved test-well and training-well adaptation.The DA neural network model developed for well-log normalization and interpretation can perform both linear and nonlinear well-log normalization by considering the joint distribution of all types of well logs and formation properties. To train the DA model, classical machine-learning models or classical petrophysical models are first trained to minimize the prediction error of formation properties in the training data set; then the adversarial model is trained to normalize well logs in the test set, such that the joint distribution of normalized well logs and formation property estimates of the test data set reproduce those of the training data set. The linear constraint model uses an ensemble of predictions from linear models to constrain both well-log normalization and interpretation. To identify wells with stationary formation properties as well as well logs, the divergence-based type well identification method is developed to choose type wells for wells requiring correction based on well-log statistical similarity instead of closeness of wells.We apply the developed methods to improve the accuracy of well-log normalization and the estimation of permeability in a carbonate reservoir. Six types of well logs and over 9000 feet of core measurements from 30 wells drilled between 1980s and 2010s in the Seminole San Andres Unit are available to validate the new multi-well interpretation workflow. Our interpretation models is flexible to integrate any types of classical machine-learning methods and petrophysical assumptions for robust petrophysical estimations. In comparison to classical machine-learning models with no normalization, with two-point scaling normalization and with linear constraints, the DA method yields better performance, e.g., the mean-squared error of permeability estimation decreases by approximately 20-50%. Our interpretation workflow can be applied to other stationary signal and image processing problems to mitigate errors introduced by biased measurements, and to better adapt models calibrated with data from one field to other neighboring fields.

show abstract

Machine learning assisted history matching for a deepwater lobe system

Cited by 24 publications

References 36 publications

Applications of Machine Learning in Subsurface Reservoir Simulation—A Review—Part I

Applications of Machine Learning in Subsurface Reservoir Simulation—A Review—Part I

Data‐Worth Analysis for Heterogeneous Subsurface Structure Identification With a Stochastic Deep Learning Framework

Reducing the Uncertainty of Multi-Well Petrophysical Interpretation from Well Logs via Machine-Learning and Statistical Models

Contact Info

Product

Resources

About