Integrating stimulation practices with geo-mechanical properties in liquid-rich plays of Eagle Ford Shale

Many unconventional reservoirs exhibit a high level of vertical heterogeneity in terms of petrophysical and geo-mechanical properties. These properties often change on the scale of centimeters across rock types or bedding, and thus cannot be accurately measured by low-resolution petrophysical logs. Nonetheless, the distribution of these properties within a flow unit can significantly impact targeting, stimulation and production. In unconventional resource plays such as the Austin Chalk and Eagle Ford shale in south Texas, ash layers are the primary source of vertical heterogeneity throughout the reservoir. The ash layers tend to vary considerably in distribution, thickness and composition, but generally have the potential to significantly impact the economic recovery of hydrocarbons by closure of hydraulic fracture conduits via viscous creep and pinch-off. The identification and characterization of ash layers can be a time-consuming process that leads to wide variations in the interpretations that are made with regard to their presence and potential impact. We seek to use machine learning (ML) techniques to facilitate rapid and more consistent identification of ash layers and other pertinent geologic lithofacies. This paper involves high-resolution laboratory measurements of geophysical properties over whole core and analysis of such data using machine-learning techniques to build novel high-resolution facies models that can be used to make statistically meaningful predictions of facies characteristics in proximally remote wells where core or other physical is not available. Multiple core wells in the Austin Chalk/Eagle Ford shale play in Dimmitt County, Texas, USA were evaluated. Drill core was scanned at high sample rates (1 mm to 1 inch) using specialized equipment to acquire continuous high resolution petrophysical logs and the general modeling workflow involved pre-processing of high frequency sample rate data and classification training using feature selection and hyperparameter estimation. Evaluation of the resulting training classifiers using Receiver Operating Characteristics (ROC) determined that the blind test ROC result for ash layers was lower than those of the better constrained carbonate and high organic mudstone/wackestone data sets. From this it can be concluded that additional consideration must be given to the set of variables that govern the petrophysical and mechanical properties of ash layers prior to developing it as a classifier. Variability among ash layers is controlled by geologic factors that essentially change their compositional makeup, and consequently, their fundamental rock properties. As such, some proportion of them are likely to be misidentified as high clay mudstone/wackestone classifiers. Further refinement of such ash layer compositional variables is expected to improve ROC results for ash layers significantly.

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Effect of Natural Fractures on Eagle Ford Shale Mechanical Properties

10.2118/170651-ms ◽

2014 ◽

Cited By ~ 8

Author(s):

Dandan Hu ◽

Luis Matzar ◽

Vladimir N. Martysevich

Keyword(s):

Mechanical Properties ◽

Natural Fractures ◽

Eagle Ford Shale ◽

Eagle Ford

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Static and Dynamic Mechanical Properties of Organic-Rich Gas Shale

Geofluids ◽

10.1155/2021/6695975 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Hui Li ◽

Chi Dong ◽

Hongwei Yu ◽

Xin Zhao ◽

Yan Li ◽

...

Keyword(s):

Mechanical Properties ◽

Young’S Modulus ◽

Young's Modulus ◽

Dynamic Properties ◽

Confining Pressure ◽

Dynamic Mechanical Properties ◽

Eagle Ford Shale ◽

Eagle Ford ◽

Dynamic Mechanical ◽

Rock Mechanical Properties

Rock mechanical properties are critical for drilling, wellbore stability, and well stimulation. There are usually two laboratory methods to determine rock mechanical properties: static compression tests and acoustic velocity measurements. Rocks are heterogeneous, so there are significant differences between static elastic constants and the corresponding dynamic ones. Usually, static test results are more representative than dynamic methods but the static tests are time consuming and costly. Dynamic methods are nondestructive and less expensive, which are practical in the laboratory and field. In this paper, we compare the static and dynamic elastic properties of Eagle Ford Shale by triaxial compressive tests and ultrasonic velocity tests. Correlations between static and dynamic elastic properties are developed. Conversion from dynamic mechanical properties to static mechanical properties is established for better estimating reservoir mechanical properties. To better understand the relationship of static and dynamic mechanical properties, 30 Eagle Ford Shale samples were tested. According to the test results, the dynamic properties are considerably different from the static counterparts. For all tested samples, static Young’s modulus is lower than dynamic Young’s modulus, ranging from 55% to 90%. The difference of the static and dynamic Young’s moduli decreases with the increasing of confining pressure. The reason may be because the microcracks closed in high confining pressure. Correlations between static and dynamic Young’s modulus are developed by regression analysis, which are crucial to understand the rock mechanical properties and forecast reservoir performance when direct measurement of static mechanical properties is not available or expensive. There are no strong correlations between static and dynamic Poisson’s ratios observed for the tested samples. Two potentially major reasons for the discrepancy of the static and dynamic properties of Eagle Ford Shale are discussed. Lithology and heterogeneity may be the inherent reasons, and external causes are probably the difference in strain amplitude and frequency.

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