A Comprehensive Workflow for Reservoir Characterization and History Matching Applicable to Cretaceous Naturally Fractured Reservoirs in Mexico

Mehranfar, Reza; Larez, C. J.; Paredes, J. E.; Villasana, J. C.

doi:10.2118/177053-ms

SPE Latin American and Caribbean Petroleum Engineering Conference 2015

DOI: 10.2118/177053-ms

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A Comprehensive Workflow for Reservoir Characterization and History Matching Applicable to Cretaceous Naturally Fractured Reservoirs in Mexico

Reza Mehranfar

C. J. Larez

J. E. Paredes

et al.

Abstract: Most of the Cretaceous fractured reservoirs in Mexico are producing from reservoirs with very poor matrix quality and therefore matrix, for the most part, cannot support the fluid flow into fracture and further on towards the wellbore. Considering other important factors like reservoir rock wettability, which is supposed to be oil wet for this type of rock, small matrix block height, and possible capillary continuity between blocks, the effective production mechanism from these reservoirs (fluid transfer from … Show more

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Cited by 3 publications

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Application of Machine-Learning to Construct Simulation Models from High-Resolution Fractured Formation

Santoso

Hoteit

2019

Day 2 Tue, November 12, 2019

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In modeling fractured reservoirs, outcrops may offer useful insights about the subsurface characterization of the heterogeneous rock formation. They provide analogs that could be replicated in the reservoir to capture the fracture and matrix characteristics, which are crucial to assess the governing recovery mechanisms. Constructing outcrop-based reservoir models is a labor-intensive process, which is subject to personal interpretation and error. In this work, we propose a novel workflow for modeling fractured reservoirs within a deep learning framework. The workflow consists of three main steps that include fracture network recognition to map explicitly the fractures from digital images, fracture characterization to provide an assessment of the fracture effective hydraulic apertures, and reservoir model construction to integrate the multi-scale data and construct the up-scaled simulation model. In this paper, we focus on the first step in the workflow. The fracture network recognition starts with segmentation for the images of the fractured formation. The ultimate objective is to identify the fractures from RGB, greyscale, or hyperspectral images. We developed a U-Net-based algorithm to perform the segmentation using 64×64 pixel-resolution. This resolution is carefully selected to accelerate the fracture recognition process and to narrow down the variability in the training set. The inputs are images of the fractured medium with any resolution which are pre-processed before feeding it to the recognition process. The output is a list of the identified fractures, where each fracture is composed of a set of segments, and each segment is defined by the coordinates of its end-points. The output format could be readily processed by any fracture modeling software. We demonstrate our workflow to recognize and identify fractures from different 2D images, where we discuss the machine-learning (ML) training and testing stages. The algorithm shows accurate predictions and identifications for the fractures. This workflow has the potential to be extended and applied at the field scale.

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Application of Machine-Learning to Construct Simulation Models from High-Resolution Fractured Formation

Santoso

Hoteit

2019

Day 2 Tue, November 12, 2019

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Uncertainty Quantification and Optimization of Deep Learning for Fracture Recognition

Santoso

Alsinan

et al. 2021

SPE Middle East Oil &Amp; Gas Show and Conference

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Automatic fracture recognition from borehole images or outcrops is applicable for the construction of fractured reservoir models. Deep learning for fracture recognition is subject to uncertainty due to sparse and imbalanced training set, and random initialization. We present a new workflow to optimize a deep learning model under uncertainty using U-Net. We consider both epistemic and aleatoric uncertainty of the model. We propose a U-Net architecture by inserting dropout layer after every "weighting" layer. We vary the dropout probability to investigate its impact on the uncertainty response. We build the training set and assign uniform distribution for each training parameter, such as the number of epochs, batch size, and learning rate. We then perform uncertainty quantification by running the model multiple times for each realization, where we capture the aleatoric response. In this approach, which is based on Monte Carlo Dropout, the variance map and F1-scores are utilized to evaluate the need to craft additional augmentations or stop the process. This work demonstrates the existence of uncertainty within the deep learning caused by sparse and imbalanced training sets. This issue leads to unstable predictions. The overall responses are accommodated in the form of aleatoric uncertainty. Our workflow utilizes the uncertainty response (variance map) as a measure to craft additional augmentations in the training set. High variance in certain features denotes the need to add new augmented images containing the features, either through affine transformation (rotation, translation, and scaling) or utilizing similar images. The augmentation improves the accuracy of the prediction, reduces the variance prediction, and stabilizes the output. Architecture, number of epochs, batch size, and learning rate are optimized under a fixed-uncertain training set. We perform the optimization by searching the global maximum of accuracy after running multiple realizations. Besides the quality of the training set, the learning rate is the heavy-hitter in the optimization process. The selected learning rate controls the diffusion of information in the model. Under the imbalanced condition, fast learning rates cause the model to miss the main features. The other challenge in fracture recognition on a real outcrop is to optimally pick the parental images to generate the initial training set. We suggest picking images from multiple sides of the outcrop, which shows significant variations of the features. This technique is needed to avoid long iteration within the workflow. We introduce a new approach to address the uncertainties associated with the training process and with the physical problem. The proposed approach is general in concept and can be applied to various deep-learning problems in geoscience.

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Application of Historical Pressures for Mapping Reservoir Heterogeneity in Brown Fields

Kayode

Stephen²

2023

Day 1 Tue, January 24, 2023

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In a homogeneous non-faulted reservoir where oil production has been ongoing long enough for a pseudo steady-state pressure regime to exist, the time-lapse shut-in pressures for all wells are expected to be similar in magnitude and trend. This gives a cluster of similar lines on a plot of shut-in bottom hole pressure over time. By contrast, heterogeneous non-faulted reservoirs can exhibit variations in these trends. Traditionally, mapping a reservoir's heterogeneity requires the use of seismic or facies data. This study aims to investigate whether the differences in pressure trends observed in heterogeneous reservoirs can be used to map reservoir heterogeneities. Numerical experiments are performed using hypothetical models to study the impact that stepwise and continuous permeability variations, the permeability ratio, duration of shut-in, and flowrate all have on the time-lapse pressure trends of wells located within regions of similar and different permeability magnitudes. The time-lapse shut-in bottom-hole pressures for all wells are plotted on the same axis to assess cluster differentiation. Closed polygons are drawn around the wells within each differentiated cluster. The simulation results indicate that pressure cluster differentiation typically implies heterogeneity, whereas time-lapse pressure clustering does not necessarily imply homogeneity. Therefore, in a reservoir where time-lapse pressure cluster differentiation is observed, mapping the spatial locations of wells within each pressure cluster would result in a reservoir heterogeneity map comparable to what is obtained traditionally from facies and seismic maps as part of geological modeling. A key contribution of this study is the development of an alternative spatial heterogeneity map, based on dynamic data (pressure), that can be used as a trend map for guiding 3D model property distributions. The application of the alternative spatial heterogeneity map for 3D geo-model property distributions ensures that important geo-model connectivity patterns are represented, facilitating subsequent history-matching efforts.

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A Comprehensive Workflow for Reservoir Characterization and History Matching Applicable to Cretaceous Naturally Fractured Reservoirs in Mexico

Cited by 3 publications

References 2 publications

Application of Machine-Learning to Construct Simulation Models from High-Resolution Fractured Formation

Application of Machine-Learning to Construct Simulation Models from High-Resolution Fractured Formation

Uncertainty Quantification and Optimization of Deep Learning for Fracture Recognition

Application of Historical Pressures for Mapping Reservoir Heterogeneity in Brown Fields

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