Construction of the Midge History Match Model

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Parametrization of stochastic inputs using generative adversarial networks with application in geology

We investigate artificial neural networks as a parametrization tool for stochastic inputs in numerical simulations. We address parametrization from the point of view of emulating the data generating process, instead of explicitly constructing a parametric form to preserve predefined statistics of the data. This is done by training a neural network to generate samples from the data distribution using a recent deep learning technique called generative adversarial networks. By emulating the data generating process, the relevant statistics of the data are replicated. The method is assessed in subsurface flow problems, where effective parametrization of underground properties such as permeability is important due to the high dimensionality and presence of high spatial correlations. We experiment with realizations of binary channelized subsurface permeability and perform uncertainty quantification and parameter estimation. Results show that the parametrization using generative adversarial networks is very effective in preserving visual realism as well as high order statistics of the flow responses, while achieving a dimensionality reduction of two orders of magnitude

Neural network applications to reservoirs: Physics-based models and data models

International audienceEditoria

A study of machine learning models application for porosity prediction using petrophysical well logs. Case Study: The Brent Group – Statfjord field

The use of machine learning algorithms for predictive analytics is making a growing impact in the field of petroleum geosciences. With the increasing cost and time-related factors for obtaining accurate porosity measurements from well logging and coring operations, machine learning (ML) provides a more economical and efficient solution to this challenge. In this thesis, various ML models are applied to predict porosity in a well penetrating the reservoir interval of the Brent Group to Top Cook formation. The study area is the Statfjord field, located in the Norwegian sector of the North Sea. Statfjord produces oil and associated gas from Jurassic sandstone in the Cook formation, Brent and Statfjord Group. Sixteen wells with several well logs serve as input features to predict the porosity in a blind well 33/9-4, all located in the field. The machine learning input features are the well logs, feature engineered logs, location points and the measured depth. The logs include: caliper, resistivity, gamma-ray, sonic, density; the engineered logs include: acoustic impedance and facies; the location: x,y,z; and the well’s measured depth. The input features are varied and ingested into the ML models to estimate the porosity in the predefined reservoir interval. The predicted porosity results for the blind well indicated an excellent performance demonstrated by the Bayesian ridge regression, linear regression and random forest models compared to the other ML models used in this study. These three algorithms are highly effective and accurate in predicting porosity with the limited range of the dataset and the results show they can be applied as a more general porosity estimation technique by varying the scale of the data samples and the number of wells

Bayesian Neural Networks for Virtual Flow Metering: An Empirical Study

Recent works have presented promising results from the application of machine learning (ML) to the modeling of flow rates in oil and gas wells. Encouraging results and advantageous properties of ML models, such as computationally cheap evaluation and ease of calibration to new data, have sparked optimism for the development of data-driven virtual flow meters (VFMs). Data-driven VFMs are developed in the small data regime, where it is important to question the uncertainty and robustness of models. The modeling of uncertainty may help to build trust in models, which is a prerequisite for industrial applications. The contribution of this paper is the introduction of a probabilistic VFM based on Bayesian neural networks. Uncertainty in the model and measurements is described, and the paper shows how to perform approximate Bayesian inference using variational inference. The method is studied by modeling on a large and heterogeneous dataset, consisting of 60 wells across five different oil and gas assets. The predictive performance is analyzed on historical and future test data, where an average error of 4-6% and 8-13% is achieved for the 50% best performing models, respectively. Variational inference appears to provide more robust predictions than the reference approach on future data. Prediction performance and uncertainty calibration is explored in detail and discussed in light of four data challenges. The findings motivate the development of alternative strategies to improve the robustness of data-driven VFMs.Comment: 34 pages, 11 figure

Application of Bayesian in determining productive zones by well log data in oil wells

International audienceExploration specialists conventionally utilize a cut-off-based method tofind productive zones inside the oil wells. Using conventional method, payzones are separated crisply from non-pay zones by applying cut-off values onsome petrophysical features.In this paper, a Bayesian technique is developed to find productivezones (net pays), and Bayesian Network is used to select the most appropriateinput features for this newly developed method. So, two Bayesian methodswere developed: the first one with conventional pay determination inputs(shale percent, porosity and water saturation), the other with two inputs,selected by Bayesian Network (porosity and water saturation). Twodeveloped Bayesian methods are applied on well log dataset of two wells:one well is dedicated for training and testing Bayesian methods, the other forchecking generalization ability of the proposed methods. Outputs of twopresented methods were compared with the results of conventional cut-offbasedmethod and production test results (i.e. a direct procedure to checkvalidation of proposed methods).Results show that the most prominent advantage of developedBayesian method is determination of net pays fuzzily with no need to identifycut-offs, in addition to higher precision of classification: nearly 30%improvement in precision of determining net pays of first well (training well),and about 50% improvement in precision of determining productive zonesthrough the generalizing well