Summary Knowledge of rock properties is essential to predict and optimize the performance of oil and gas reservoirs by means of the reduction of the uncertainty pertaining to standard subsurface issues such as the mechanical integrity of the borehole (Tiab and Donaldson 1996; Moos et al. 2003), the risk of sanding (Tronvoll et al. 2004), and the geometry and efficiency of hydraulic fractures. These properties are evaluated by combining different field-measurement techniques (wireline logs, results of well tests, seismic surveys) and laboratory-test results (Archie 1942, 1950; Serra 1986; Bassiouni 1994). When cores are available, empirical models are built from correlations derived between well logs and laboratory measurements to estimate rock properties in noncored wells. The validity of these empirical models is often limited to specific litho-facies (see reviews by Chang et al. 2006; Blasingame 2008; Khaksar et al. 2009), which makes the identification of lithofacies a necessity before applying the model for predictions in uncored wells (Massonnat 1999). Because of the heterogeneity of rocks (Haldorsen 1996), with characteristic length scales commonly smaller than the resolution of wireline logs or even the core-plug size, the robustness of correlations is determined by how plug samples capture the dispersion in rock properties over the lithofacies under consideration. The correlation between a very localized core-plug measurement and a low-resolution wireline log with inherent low-pass filtering properties raises issues related to the upscaling of a property from one length scale (few centimeters for core plugs) to another (up to 1 m for wireline log). As an illustration, consider the high-resolution, continuous profile X, where the variations of the measured property are quantified for length scales smaller than typical plug sizes. We filter this data to produce the profiles X5 and X50 (the subscript stands for the length scale in centimeters at which the signal is averaged out) with lower spatial resolutions similar to the plug and the well-log resolutions, respectively (Fig. 1). The resulting crossplot, shown in Fig. 2, of X5 vs. X50 exhibits a cloud of points in which the dispersion is governed by the properties of the signal (the degree of heterogeneity or the frequency content) and the difference between the two resolution length scales. Two linear-fit optimizations were carried out with the low-resolution-data X50 and the high-resolution-data X5 as the dependent variables, respectively. It is interesting to note that these linear fits yield different results, with a slope of 0.96 in the first case and 0.69 in the second case. This is a mathematical artifact caused by the minimization process inherent in the search for the best linear fit, which is most commonly a minimization of the vertical distance between the representative data points and the best-fit line. On the basis of this result, it should always be advisable to select the high-resolution data (plug) as the dependent variable. Discrete sampling (i.e., plugging) and the dispersion caused by the difference in resolution scales of two measurements are two important root causes of the errors often seen in correlations between two variables. The examples shown in Fig. 2 illustrate how the correlations derived from several sampling schemes can deviate from the expected one-to-one relation between the two variables. To circumvent these issues, petrophysicists usually select large quantities of plugs to build representative statistical data sets, with the hope that they are large enough to attenuate the effects listed previously. However, extensive plugging strategies imply longer lead times and higher costs, and are therefore not always viable (e.g., in the cases of rock-mechanics testing or special-core-analysis programs). As an illustration, consider the modeling of the variations of rock strength, one of the key geomechanical properties along a well trajectory. Such an exercise relies heavily on correlations derived between well logs and laboratory tests (uniaxial or triaxial compressive tests), because there is no wireline log providing a direct measure of a mechanical property related to strength. In their comprehensive review of existing literature, Khaksar et al. (2009) listed approximately 40 models designed to derive strength properties from wireline logs. The authors showed that the relevance of these as empirical is limited to specific rock types. A broader application of these models would require the considerations of additional complexity such as the coexistence of several facies within the same data set or the impact of diagenesis on petrophysical variability within one facies. The elements of reflection introduced previously all suggest that a continuous measure of a physical property such as the strength profiles generated from the scratch test, which provides some useful elements for the mapping of rock heterogeneity, could partially fill the gap between measurements on plugs and well logs and help with the optimization of the selection of plug samples. In the main sections of this paper, we first describe briefly the scratch test and outline the key intrinsic benefits of the test. We then discuss how standard and special core analysis could benefit most from all the features of the scratch test when introduced at a very early stage of the work flows. In particular, we illustrate with some examples how rock-strength profiles averaged to the relevant length scale can be correlated with other petrophysical properties either measured on core plugs or inferred from well logs.
Knowledge of accurate rock strength is essential for in situ stress estimation, wellbore stability analysis, sand production prediction and other geomechanical applications. Reliable quantitative data on rock strength can only be obtained from cores. However, cores are limited, discontinuous and often biased. Consequently, rock strength evaluation is primarily based on log strength indicators, calibrated where possible against limited core measured values. There are a number of published log-core strength correlations that can be used for rock strength modelling. These empirical relationships are developed for specific rock type, age, depth range and field. Their general applications, therefore, need to be critically assessed on a case by case basis. This paper briefly: (i) outlines the best practice for obtaining quality rock strength data from core tests; (ii) presents common empirical rock strength equations for sedimentary rocks and (iii) discusses ways of improving rock strength estimates.While some equations such as porosity-based or sonic log-based rock strength models work reasonably well, rock strength variations within individual rock properties show considerable scatter, indicating that most of the empirical models are not sufficiently generic to fit all rocks in the database. Like any other physical rock properties, the variation in rock strength in a given sedimentary rock is controlled by mineralogy, sedimentology and micro-structure of the rock and simple log-derived rock strength models need further modification and classification incorporating these geological characteristics.This paper has shown that when sufficient core rock strength data exists, applications of computing techniques, such as fuzzy logic and cluster pattern recognition, coupled with sedimentary facies analysis and diagenetic classification can improve strength estimation. Semi-continuous impact energy logs using portable non-destructive testing tools can be correlated with petrophysical logs to generate mechanical facies and improved sampling for conventional rock testing.
Depleted hydrocarbon reservoirs are attractive targets for short-term gas storage with frequent injection and production cycles. Optimum well completion and injection-storage-production design in depleted reservoirs would require an understanding of important rock mechanical issues. These include drilling and completion challenges of new wells in low-pressure reservoirs accounting for potential rock fatigue due to cyclic injection/depletion and loading and unloading, and determination of maximum sustainable storage pressures that would avoid fracturing and fault reactivation. This paper describes a case study from a coal seam gas project considered for supply to a liquefied natural gas plant in Australia. The paper demonstrates a systematic approach for geomechanical risk assessments for short-term gas storage in depleted sandstone reservoirs. Depleted sandstone gas reservoirs at a depth of 1,000 m with existing pressures of 150–300 psi are considered in this study. Historical and new well data including cores, well logs, drilling, and field data such as injection and minifracture (minifrac) tests are used to develop a field-specific geomechanical model. Field data and laboratory measurements of rock mechanical properties are used to define the stress path factors and the change in in situ stress with depletion and injection in sandstone reservoirs in the study area. Rock mechanics tests on representative core plugs under cyclic loading and unloading simulating operating depletion and injection pressure conditions are used to assess the level of rock fatigue and rock weakening under cyclic loading. Geomechanical analyses show that despite a low fracture gradient in depleted reservoirs and the presence of non-depleted overburden rocks, new high-angled wells can be drilled safely with a relatively low mud weight in the non-depleted sections and with air in the reservoir section. Fracturing and faulting assessments confirm the critical pressures for fault reactivation and fracturing of intact rocks are beyond the planned storage pressures, and a maximum pressure of 200–300 psi beyond the initial reservoir pressures may be possible from fracturing or fault reactivation aspects. Sand production prediction evaluations indicate that new injection-production wells can be completed with no downhole sand control due to a very low risk of sanding even after considering rock weakening associated with cyclic loading. The methodology and overall workflow presented in this paper can be applied when carrying out geomechanical risk assessments for natural gas storage in depleted reservoirs.
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