Shale elastic property relationships as a function of total organic carbon content using synthetic samples

Altowairqi, Yazeed; Rezaee, Reza; Evans, Brian; Urošević, Milovan

doi:10.1016/j.petrol.2015.06.028

Cited by 36 publications

(11 citation statements)

References 17 publications

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“…Actually, the organic carbon content in gas shale ranges from less than 1% to as high as 25% (in some organic-rich beds). [15][16][17][18] While the inorganic mineral portion of the shale, such as quartz, calcite, clay minerals, and muscovite mica account for a large proportion, 2,4,19 according to literature reports, the content of quartz could be as high as 87% in shale from the Qaidam Basin in China; 2 hence, studies about the adsorption of CO 2 and CH 4 in a quartz nanopore is meaningful.…”

Section: Introductionmentioning

confidence: 99%

Adsorption properties of CH₄ and CO₂ in quartz nanopores studied by molecular simulation

Sun

Zhao

et al. 2016

RSC Adv.

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Section: Introductionmentioning

confidence: 99%

Adsorption properties of CH₄ and CO₂ in quartz nanopores studied by molecular simulation

Sun

Zhao

et al. 2016

RSC Adv.

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“…Beginning with Kaarsberg (1959), many researchers have attempted to make artificial shales or fault gounges from mixtures of clays and other materials (Arch & Maltman, 1990;Crawford et al, 2008;Mondol et al, 2007;Revil et al, 2002;Vanorio et al, 2003;Voltolini et al, 2009), upon which measurements could be made. In order to mimic unconventional shale reservoir rocks, Luan et al (2014) and Altowairqi et al (2015) have constructed analog carbon-rich composites by compressing organic materials with clay at room temperature. Though these are important advances, the P and S wave velocities and the degree of anisotropy they observed were much less than expected for natural shales.…”

Section: Introductionmentioning

confidence: 99%

Effects of Kerogen Content on Elastic Properties‐Based on Artificial Organic‐Rich Shale (AORS)

et al. 2019

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Quantitative rock physical approaches are becoming increasingly important for both geophysical exploration and geological interpretation. One limiting factor is that experimental measurements in natural rock tend to be restricted by the availability of natural materials particularly if they are obtained from core holes. Artificial physical models that allow for control of composition and structure provide opportunities to carry out many more experiments on a range of samples with different characteristics. In this study, a hot‐pressing technique that includes temperature and pressure control is developed to construct artificial organic‐rich shales (AORS). The compositions of the AORS were developed from known natural samples and include mixtures of relevant minerals and appropriate organic matter considering grain sizes and shapes. The microstructures of the samples as observed in the scanning electron microscope and the petrophysical properties (density, porosity, permeability, and pressure sensitivity) are similar to those observed in the natural shales and support use of these samples as shale analogs. A total of 10 AORS samples with kerogen contents (KC) ranging from 1.71% to 14.8% was constructed to evaluate the effects of KC on elastic properties. A cross‐plot of the VP/VSH ratio versus the acoustic impedance indicates patterns that may be useful in remotely assessing KC content. Similarly, cross‐plots using the Lamé parameters of λρ versus μρ at various confining pressure may also be an effective tool in predicting KC. The linear correlations between the sum of weight percentage of KC and porosity and acoustic impedance tend to be strengthened during the pressurized procedure.

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“…Total Organic Carbon (TOC) is one of the crucial parameters in shale gas reservoir assessments and is also regarded as one of the key variables that directly influences the rock quality, hydraulic fracturing design, and shale gas-in-place estimations (Passey et al 2010, Sondergeld et al 2010. As a part of the rock matrix, organic matter also strongly affects the geomechanical properties of shales (Altowairqi et al 2015). In addition, organic carbon content and maturity are important factors impacting organic porosity, and controlling the absorbed gas in the shale gas reservoirs (most of the absorbed gas is occurred on organic matter) (Montgomery et al 2005, Ross andBustin 2007).…”

Section: Introductionmentioning

confidence: 99%

“…In addition, organic carbon content and maturity are important factors impacting organic porosity, and controlling the absorbed gas in the shale gas reservoirs (most of the absorbed gas is occurred on organic matter) (Montgomery et al 2005, Ross andBustin 2007). Moreover, TOC controls the micro structure, texture, porosity, permeability and wettability of the shale reservoirs (Altowairqi et al 2015, Sondergeld et al 2010, Sone and Zoback 2013, Zhang et al 2012. Thus, a reliable method for the characterization of shale organic matter and for the accurate prediction of TOC is crucial for hydrocarbon exploration and production from these unconventional reservoirs (Ding et al 2015, Wang et al 2016.…”

Section: Introductionmentioning

confidence: 99%

A new method for TOC estimation in tight shale gas reservoirs

Rezaee

Wang

et al. 2017

International Journal of Coal Geology

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Total organic carbon (TOC) estimation is significantly crucial for shale reservoir characterization. Traditional TOC estimation methods (such as Passey and Schmoker method) do not provide accurate TOC predictions in shale gas reservoirs especially for the self-generated and self-stored reservoirs. This study proposes, for the first time, a new TOC prediction method based on Gaussian Process Regression (GPR) bridging geostatistics and machine learning technique. The method utilizes a non-parametric regression approach in shale TOC predictions, and not only provides the expert solutions in high-dimension processing, small samples and non-linear problems, but also has a better adaptation and generalization ability compared with other machine learning methods. The approach accounts for all the well logging attributes and chooses the relevant logs to build TOC estimation model, and 7 different kernel functions and 5 attributes groups are analyzed to get the optimized hyperparameters in practice. Application of the developed model to two shale gas reservoirs showed that the model predicted TOC matched well with that from the laboratory measurements. The proposed model based on GPR method provides an accurate way for the TOC prediction in the tight shale gas reservoirs.

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Shale elastic property relationships as a function of total organic carbon content using synthetic samples

Cited by 36 publications

References 17 publications

Adsorption properties of CH₄ and CO₂ in quartz nanopores studied by molecular simulation

Adsorption properties of CH₄ and CO₂ in quartz nanopores studied by molecular simulation

Effects of Kerogen Content on Elastic Properties‐Based on Artificial Organic‐Rich Shale (AORS)

A new method for TOC estimation in tight shale gas reservoirs

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Shale elastic property relationships as a function of total organic carbon content using synthetic samples

Cited by 36 publications

References 17 publications

Adsorption properties of CH4 and CO2 in quartz nanopores studied by molecular simulation

Adsorption properties of CH4 and CO2 in quartz nanopores studied by molecular simulation

Effects of Kerogen Content on Elastic Properties‐Based on Artificial Organic‐Rich Shale (AORS)

A new method for TOC estimation in tight shale gas reservoirs

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Product

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Adsorption properties of CH₄ and CO₂ in quartz nanopores studied by molecular simulation

Adsorption properties of CH₄ and CO₂ in quartz nanopores studied by molecular simulation