Characterization of organic-rich shales for petroleum exploration &amp; exploitation: A review-Part 3: Applied geomechanics, petrophysics and reservoir modeling

Wood, David A.; Hazra, Bodhisatwa

doi:10.1007/s12583-017-0734-8

Cited by 27 publications

(7 citation statements)

References 122 publications

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“…Rock cuttings are extracted and used as a reliable source for estimation of formation characteristics, albeit their reliability is still under challenge. 7 However, even though some boreholes are cored and limited cores are recovered, one should consider the damaging effects of drilling mud and its associated chemicals on the retrieved core samples. They are susceptible to alteration as a result of cooling and depressurization after retrieval to the surface, resulting in volume expansion and generation of desiccation and microfractures.…”

Section: Introductionmentioning

confidence: 99%

“…Shale reservoirs are developed by horizontal wells. Rock cuttings are extracted and used as a reliable source for estimation of formation characteristics, albeit their reliability is still under challenge . However, even though some boreholes are cored and limited cores are recovered, one should consider the damaging effects of drilling mud and its associated chemicals on the retrieved core samples.…”

Section: Introductionmentioning

confidence: 99%

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Use of Outcrop as Substitute for Subsurface Shale: Current Understanding of Similarities, Discrepancies, and Associated Challenges

et al. 2021

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Full coring in shale reservoirs is challenging, time consuming, and risky due to fragility. Therefore, shale outcrop analogs are often used as an alternative to enhance the understanding of corresponding subsurface reservoir formation multifaceted mineralogy and rock characteristics. As consequence, many researchers perform their fundamental experimental studies on the core samples collected from outcrops when the subsurface unconventional shale samples are scarce. An assumption is often made that the rock composition and properties of outcrop and subsurface samples remain the same which raises some serious concerns and demands a thorough review of published research to check the validity of this approach to characterize shale reservoirs. The high degree of lateral and vertical heterogeneity and anisotropy, weathering, contamination, and postdepositional tectonic activities further add to the major challenges. In this literature-based study, the authors revisit the current understanding and challenges of using shale outcrops to complement the available subsurface samples and use them for the preliminary evaluation of underexploration and undeveloped unconventional shale reservoirs. The similarities and discrepancies of mineralogical, petrophysical, geochemical, and geomechanical properties are discussed. Finally, some concluding remarks and recommendations are presented.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Use of Outcrop as Substitute for Subsurface Shale: Current Understanding of Similarities, Discrepancies, and Associated Challenges

et al. 2021

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“…Moreover, the testing data contains a lot of interference signals, singular signals, and information about the ground operations and the activities of the staff due to the temporary operation flow adjustment or the occurrence of uncertain events at strata and borehole locations [2]. Even when the data is from the same operation stage but different testing wells, there exists a large difference in the length and the curve shape.…”

Section: Introductionmentioning

confidence: 99%

Automatic Deep Vector Learning Model Applied for Oil-Well-Testing Feature Mining, Purification and Classification

Feng

Ren

et al. 2020

IEEE Access

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Well-testing stage analysis is an important way for oilfield operation state decision-making and reservoir management. However, due to the variability and nonlinearity of the downhole data caused by the complex exploration activities and the differences of petroleum type and geological conditions, the classical methods are ineffective in feature extraction, learning network construction, and classifier optimization. In this work, we propose a new well-testing stage classification method based on a deep vector learning model (DVLM). The novelty of this study lies in the combination of multi-feature extraction, deep learning and feature vector mapping. The proposed method can overcome the problems of poor feature representation ability and poor classification model generalization ability in the existing machine learning methods, which mainly caused by the non-optimized training network structure and the unreasonable classifier design,. Firstly, the initial features are obtained by four classical methods. Then a five layers deep belief network embedded with the mutual information coefficient method is implemented for further feature extraction and purification. Finally, the optimized learning vector quantization classifier outputs the predicted tags. For model training and testing 572 field samples total of 4004 data streams are used. By considering the classification errors and accuracy metrics, the neurons number of deep learning network and the classifier are tuned, and an optimal and stable framework is obtained. Comparative experimental results with several classical integration models show that the proposed model achieves the highest classification accuracy of 98.065% as well as the least of features (nine). The results demonstrate that the proposed model has excellent performance in improving the classification accuracy and completing the feature compression. Moreover, the proposed model has very important practical significance for guiding the automatic analysis and processing oil and gas data. INDEX TERMS Well-testing stage analysis; Feature extraction; Deep belief network; Learning vector quantization.

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“…2 Micro-seismic interpretation may predict the general area of fractures. 3 However, it cannot give a definitive answer to how the hydraulic fractures distribute and whether it reclose after fracturing, let alone whether the fractures can establish flow connection. Therefore, we integrated micro-seismic interpretation results with history matching to predict hydraulic fracture parameters.…”

Section: Introductionmentioning

confidence: 99%

Parameter Prediction of Hydraulic Fracture for Tight Reservoir Based on Micro-Seismic and History Matching

Zhang

Li³

et al. 2018

Fractals

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Hydraulic fracturing is an important measure for the development of tight reservoirs. In order to describe the distribution of hydraulic fractures, micro-seismic diagnostic was introduced into petroleum fields. Micro-seismic events may reveal important information about static characteristics of hydraulic fracturing. However, this method is limited to reflect the distribution area of the hydraulic fractures and fails to provide specific parameters. Therefore, micro-seismic technology is integrated with history matching to predict the hydraulic fracture parameters in this paper. Micro-seismic source location is used to describe the basic shape of hydraulic fractures. After that, secondary modeling is considered to calibrate the parameters information of hydraulic fractures by using DFM (discrete fracture model) and history matching method. In consideration of fractal feature of hydraulic fracture, fractal fracture network model is established to evaluate this method in numerical experiment. The results clearly show the effectiveness of the proposed approach to estimate the parameters of hydraulic fractures.

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Characterization of organic-rich shales for petroleum exploration & exploitation: A review-Part 3: Applied geomechanics, petrophysics and reservoir modeling

Cited by 27 publications

References 122 publications

Use of Outcrop as Substitute for Subsurface Shale: Current Understanding of Similarities, Discrepancies, and Associated Challenges

Use of Outcrop as Substitute for Subsurface Shale: Current Understanding of Similarities, Discrepancies, and Associated Challenges

Automatic Deep Vector Learning Model Applied for Oil-Well-Testing Feature Mining, Purification and Classification

Parameter Prediction of Hydraulic Fracture for Tight Reservoir Based on Micro-Seismic and History Matching

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