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Dash, Sabyasachi (The University of Texas at Austin) | Garcia, Artur Posenato (The University of Texas at Austin) | Hernandez, Laura M (The University of Texas at Austin) | Heidari, Zoya (The University of Texas at Austin)
Reliable fluid saturation assessment in organic-rich mudrocks has been a challenge for the oil and gas industry. Composition and spatial distribution of rock components have a significant impact on electrical resistivity and, thus, on hydrocarbon reserves estimates. Clays are typically considered, in resistivity models, to be distributed in laminated or dispersed forms. Additionally, conventional resistivity models do not incorporate conductive components other than brine. Such assumptions can lead to uncertainty in fluid saturation assessment in organic-rich mudrocks. Hence, we introduce a well-log-based workflow that quantitatively assimilates the type and spatial distribution of all conductive components to improve reserves evaluation in organic-rich mudrocks and demonstrate its field application.
The introduced workflow consists of two inversion algorithms; the first estimates geometry-dependent coefficients (depolarization factors or geometric model parameters), and the second estimates water saturation in surrounding wells. Geometric model parameters are determined in a formation-by-formation basis by minimizing the difference between the estimated resistivity (derived from Pore Combination Modeling) and resistivity measurements. Inputs to the inversion algorithms include volume concentrations of minerals, estimated from the multi-mineral analysis. The petrophysical model considers that brine forms the conductive background to which conductive (e.g., clay, pyrite, kerogen) and non-conductive (e.g., grains, hydrocarbon) components are incorporated. Other inputs are conductivity of rock components and porosity obtained from lab experiments and interpretation of well logs.
We successfully applied the workflow to two wells in the Eagle Ford and the Woodford formations. The formation-by-formation inversion showed a variation in geometric model parameters in different petrophysical zones, resulting in improved water saturation estimates. A comparison of the results obtained from the new workflow against those from Waxman-Smits and Archie’s models indicated a relative improvement in saturation estimates of 9.5% and 26.3% in the Eagle Ford formation. The improvement can be enhanced in formations with larger fractions of conductive components. Results confirmed that the new workflow improves the reliability of water saturation estimates in organic-rich mudrocks, which has been a challenge for the oil and gas industry. In contrast to conventional techniques, the new method does not need water saturation obtained from core measurements for calibration efforts. All the parameters in the new workflow are geometry- or physics-based. We verified that formation-based geometric model parameters in the Eagle Ford formation were consistent in both wells, which is promising for calibration-free assessment of water/hydrocarbon saturation in the field-scale domain using electrical resistivity measurements. Finally, the new method minimizes the need for expensive and time-consuming core measurements, which is a unique contribution of this work.
Abstract Organic-rich mudrocks are complex in terms of rock fabric (i.e., the spatial distribution of rock components), which impacts electrical resistivity measurements and, therefore, estimates of hydrocarbon reserves. Conventional resistivity-saturation-porosity methods for assessment of water/hydrocarbon saturation do not reliably incorporate the spatial distribution of rock components and pores in the assessment of fluid saturation. Extensive calibration efforts are required for indirectly projecting the impact of rock fabric on resistivity models. For instance, none of the existing shaly-sand models incorporate a realistic distribution of clay network. This might be acceptable in conventional reservoirs. However, oversimplifying assumptions can cause significant uncertainty in reserves evaluation in organic-rich mudrocks. It should be noted that even the methods which incorporate the realistic distribution of rock components are difficult to calibrate. To address the aforementioned challenge, we introduce a joint interpretation of conventional resistivity and resistivity image logs to improve water saturation assessment by honoring the type of rock component, the spatial distribution of the conductive and non-conductive rock components, and the volumetric concentration of fluids and minerals in the rock. Borehole image logs are a source of high-resolution continuous rock sequence records and can provide detailed rock-fabric-related features. In this paper, we propose a method for the estimation of lamination density and mean resistivity value from image logs within each rock type. These fabric-related features are used to quantify the geometric model parameters for each conductive component of the rock. We use these geometric model parameters as inputs to a new resistivity model that considers volumetric concentration and spatial distribution of rock components for a depth-by-depth assessment of water saturation. The other inputs to the workflow are the volumetric concentration of conductive and non-conductive rock components, electrical conductivity of rock components, and porosity estimates from the joint interpretation of well logs. We successfully applied the proposed workflow to a dataset from the Wolfcamp formation in the Permian Basin in which resistivity image logs were available. We observed a measurable variation in estimated image-log-based geometric model parameters, which were in agreement with the visual content of the images. Incorporation of the estimated rock-class-based geometric model parameters in the resistivity model improved water saturation assessment. Results demonstrated a relative improvement in water saturation estimates of 44.2% and 59.1% against Waxman-Smits and Archie's models, respectively. We then used the estimated geometric model parameters for each rock type for a depth-by-depth assessment of water saturation in one additional well without image logs. This led to a faster and more reliable assessment of water saturation within a certain distance from the well with image logs, where the rock types remain comparable. This distance can be evaluated using variogram analysis. We demonstrated that using the estimated geometric model parameters could improve estimates of hydrocarbon reserves in the Permian Basin by approximately 34%. It should be noted that the proposed method for assessment of geometric model parameters is completely based on the actual spatial distribution of rock components and does not require core-based calibration efforts.
Excess conductivity in shaly sandstones complicates the interpretation of resistivity logs for water saturations. A new laboratory procedure has been developed to provide rapid, yet highly accurate assessment of excess conductivity effects in core samples. The basis of the technique is conductivity measurements on samples saturated with dilute brine.
Well logging tools measure the physical properties of rock formations surrounding a borehole. Quantities such as natural radioactivity, sonic velocity and resistivity of the rock require interpretation to convert them to information useful in formation evaluation, such as the porosity and hydrocarbon content of the reservoir. Resistivity, or its reciprocal, conductivity, is the rock property most often used to evaluate water property most often used to evaluate water saturation within the pores of a hydrocarbon reservoir rock.
Interpretation of electrical data is based on the contrasting electrical properties of salt water and oil or gas. In the simplest case, the rock matrix and the hydrocarbon are electrical insulators, and the Archie equations can be used to calculate water saturation, Sw, from the conductivity originating with ions in the formation brine:
1/n Sw = [FCt/Cw] ..................(1)
F = Cw/Co = a. ....................(2)
F = formation factor Cw = formation brine conductivity Co = rock conductivity at Sw=1 Ct = rock conductivity at partial saturation = porosity a, m and n are empirical constants
However, sandstone reservoir rocks rarely have a perfectly insulating rock grain matrix, because the perfectly insulating rock grain matrix, because the surfaces of minerals which form the pore walls have mobile cations associated with them. The Archie equations are not strictly applicable if the "excess" conductivity (X) associated with these mobile surface cations forms a significant fraction of the overall electrical response. In the hydrocarbon zone of a shaly sand, a mistakenly high water saturation can be calculated, unless an appropriate correction is employed for this effect.
The surface conduct ion effect is usually referred to as a shaly sand problem, because the major source of mobile surface cations in sandstones is authigenic clay minerals. Clay crystals which grow within pores have a high surface area, so a small mass pores have a high surface area, so a small mass fraction of clay can cause high excess conductivity.
Electrical logging tools cannot distinguish between conduction by the surface cations, and that by ions in the formation brine. Thus, laboratory core analysis is required to quantify excess conductivity effects. The usual rapid analysis technique to assess excess conductivity involves cation exchange capacity (CEC) measurements on cleaned, disaggregated rock. The number of exchangable, or mobile, surface ions per unit pore volume (Qv) may be converted to an excess conductivity value using the theory of Waxman and Smits.