Unconventional shale gas reservoirs are economically viable hydrocarbon prospects, and their development has rapidly increased in North America. These reservoirs must be routinely drilled horizontally and hydraulically fracture stimulated to maximize production rates. Identification of the different chemostratigraphic units or lithofacies that make up these reservoirs is crucial for devising completion strategies because some lithofacies are more favorable to gas recovery due to their organic content and geomechanical characteristics.Lithofacies are indicative of eustatic changes during deposition and are typical geo-markers related to the preservation and amount of accumulated Total Organic Carbon (TOC) for a given basin. Gas content is related to TOC and varies according to lithofacies. Based on the mineralogical and TOC content, some lithofacies are favorable for gas production (e.g., siliceous lithofacies) and the geomechanical properties of these lithofacies often possess low fracture gradients that are conducive to forming extensive fracture fairways for recovery of gas. Other lithofacies can be fracture barriers and zones of fracture propagation attenuation (e.g., carbonate lithofacies).A shale gas facies expert system was developed with the goal of chemostratigraphically characterizing different shale plays and utilizing an integrated petrophysical reservoir evaluation approach to identify optimal completion intervals. This system can aid operators design selective completion strategies, which can potentially reduce fracturing expenses and optimize well productivity. The expert system incorporates a combination of density, neutron, acoustic, nuclear magnetic resonance and geochemical logging measurements. This system first characterizes the lithofacies based on their geochemical makeup and then flags the most favorable and unfavorable zones using a simple "stop-light" approach based on the petrophysical and geomechanical properties.
A new openhole logging service, RockViewSM, has been developed which provides lithological and quantitative mineralogical information for accurate formation evaluation. The assessment begins with elemental formation weights and follows with an interpretation of lithology and mineralogy. Lithologies are divided into general categories including sand, shale, coal, carbonates, and evaporites. Potentially identifiable minerals are quartz, potassium-feldspar, albite, calcite, dolomite, siderite, anhydrite, illite/smectite, kaolinite, glauconite, chlorite, pyrite, and others. The logging system utilizes an electronic pulsed source to send high energy neutrons into the surrounding formation1–8. These neutrons quickly lose energy as a result of scattering, after which they are absorbed by the various atoms within the ambient environment. The scattered as well as the absorbed neutrons cause the atoms of the various elements to emit gamma rays with characteristic energies. These are measured with a scintillation detector, resulting in both inelastic and capture gamma ray energy spectra. A matrix inversion spectral fit algorithm is used to analyze these spectra in order to separate the total response into its individual elemental components. The prominent measured elements associated with subsurface rock formations include calcium, silicon, magnesium, carbon, sulfur, aluminum, and iron. Potassium, thorium, and uranium are measured separately with a natural gamma ray spectroscopy instrument9–11. The tool response is characterized for each individual element by placing it into formations of known chemical composition. Interpretation of the data begins with an assessment of the elemental formation weights, which then leads to a determination of lithology and mineralogy. Each step in the process is guided by the examination of ternary plots containing selected elements. Magnesium is an extremely important part of the interpretation process since it distinguishes dolomite from calcite and helps to identify various types of clay. Data from field examples is presented in order to illustrate the effectiveness of this technology. Introduction Traditional formation evaluation using log data involved interpretation of measurements that included natural gamma ray, neutron porosity, density, and resistivity. Over the past decade, there has been an increase in the number of petrophysicists who desire additional information, including mineralogy-based logs. Such data helps resolve ambiguities in the traditional methods and opens up the possibility for new deductions to optimize hydrocarbon production. If one is concerned about calcite or anhydrite cement in a sand matrix, for example, such suspicions can be resolved through a measurement of the amounts of Ca and S. Knowledge of the formation matrix components can also be used to provide a more accurate porosity through an enhanced interpretation of the neutron and density data. In general, the interpretation of any measurement can be enhanced when the formation matrix is understood. A deductive approach to mineralogy is described herein, which begins by identifying the general lithology associated with each tool measurement as follows:Elements ? General Lithology ? Specific Lithology ? Mineralogy
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