|Theme||Visible||Selectable||Appearance||Zoom Range (now: 0)|
In this paper we propose a new workflow to perform Petrophysical Joint Inversion (PJI) of surface to surface seismic and Controlled Source ElectroMagnetic (CSEM) data, to recover reservoir properties (clay volume, porosity and saturation). Seismic and CSEM measurements provide independent physical measurements of subsurface that complement each other. In the case of well-logs, the basis of the PJI training dataset, taking advantage of such complementarity is straightforward. Indeed, elastic and electric measurements of earth properties sense the same earth volume at much the same scale. When applying the training dataset to the surface data derived geophysical attributes, the order of magnitude gap in between the scale at which those elastic and electric attributes represent the earth undermines dramatically PJI validity. Various CSEM inversion constraining methods (regularization breaks, prejudicing, use of an a priori model etc) help to reconcile seismic and CSEM resolution, but they are usually proven to be insufficient or inaccurate. In addition to these methods, we suggest adding a further downscaling step, so the recovered electric attribute resolution can be adequate with respect to the seismic one, hence fit for purpose. Such downscaling is designed to be consistent in electrical attribute space via transverse resistance within a rockphysics framework. The workflow will be demonstrated on a case study.
We present an anisotropic rock physics model which can be used to estimate velocities for different facies types (sands, shales and carbonates). The model uses a combination of the joint Self Consistent, Approximation and Differential Effective Medium model (SCA/DEM) and the Hudson model for fractures. The SCA/DEM model is used to build the frame of the rock and the Hudson model adds fractures in 3 orthogonal directions with varying concentrations inducing anisotropy. Allowing the model parameters to change gives enough flexibility to the model to model different facies including sands and carbonates. The model has been tested against sand, shale and carbonate data from well logs in the Barents Sea and the North Sea. Anisotropy for this well was estimated using the method of White (1983). Results show a good fit between the rock model and the data.
Presentation Date: Thursday, October 18, 2018
Start Time: 8:30:00 AM
Location: 202A (Anaheim Convention Center)
Presentation Type: Oral
In this paper we propose a new rock physics workflow which uses a combination of the Hashin-Shtrikman bounds (Hashin and Shtrikman, 1962) and the Joint Self Consistent Approximation (Bruggeman, 1935; Landauer, 1952; Berryman, 1995) and Differential Effective Medium model (Bruggeman, 1935; Sen
Presentation Date: Monday, October 15, 2018
Start Time: 1:50:00 PM
Location: 202A (Anaheim Convention Center)
Presentation Type: Oral
Ellis, Michelle (RSI) | MacGregor, Lucy (RSI) | Ackermann, Rolf (RSI) | Newton, Paola (RSI) | Keirstead, Robert (RSI) | Rusic, Alberto (RSI) | Bouchrara, Slim (RSI) | Alvarez, Amanda Geck (RSI) | Zhou, Yijie (RSI) | Tseng, Hung-Wen (RSI)
In this study we use Controlled Source Electromagnetic (CSEM) data, well log data and rock physics to investigate electrical anisotropy drivers in the Snøhvit area of the Barents Sea. Results show that for the shale dominated sediments electrical anisotropy varies systematically with porosity, depth and elastic properties. However there is little systematic trend with clay content.
CSEM can be used to provide higher sensitivity to hydrocarbon saturation than is possible to achieve with conventional seismic reflection data (MacGregor & Tomlinson, 2014). In CSEM’s infancy anisotropy was ignored, however, disregarding resistivity anisotropy will lead to misleading CSEM survey feasibility studies, inaccurate CSEM data analysis, inaccurate estimations of hydrocarbon saturations and, consequently, erroneous interpretations (Ellis et al., 2011). In order to improve our interpretation of CSEM data we need to understand what drives the anisotropy for a given rock type. The aim of rock physics is to understand the relationship between geophysical observations and the underlying physical properties of the rock (Mavko et al., 2009). Physical properties include properties such as porosity, mineral composition, pore-fluid composition and sediment microstructure. By using rock physics we can start to understand the controls on electrical resistivity and anisotropy in a given area. The aim of this project is to determine the controls on electrical anisotropy in the Snohvit area of the Barents Sea and forms part of a wider study of Barents Sea electrical properties (Bouchrara et al, 2015). The Barents Sea was chosen as a study area because of the current interest in the area and the rich dataset which included well logs and CSEM surveys (Figure 1). Also the Barents Sea is geologically complex – stratigraphically, structurally, and historically (Gabrielsen et al., 1990). One component of this complexity is the presence of strong anisotropy in measured and derived electrical resistivity (Fanavoll et al., 2012).
Electrical anisotropy has a strong effect on CSEM data (Ramananjaona et al, 2011), and understanding this effect is key in ensuring robust survey design and well constrained data analysis (MacGregor & Tomlinson, 2014). Electrical anisotropy can also provide key information that can be used to understand regional variations in rock physics properties as well as provide possible indications to geological drivers in an area, such as uplift. To date there have been no systematic regional studies of electrical anisotropy in background geological structure. Addressing this need, by investigating electrical anisotropy variations across the Barents Sea is one of the main goals of the industry funded ERA consortium.
Bulk anisotropy values were derived from CSEM data for each of the major stratigraphic units across the Barents Sea. This was achieved by performing 1D anisotropic inversion of CSEM data acquired around well bores, and tying the horizontal resistivity to the induction log measurements from these wells. Results were then mapped and regional trends are investigated. The modelling confirms the presence of high electrical anisotropy ratios in the Barents Sea area and a correlation between anisotropy ratio and formation age: In general the older the formation, the higher the anisotropy ratio. Although resistivity varies regionally, the variation in anisotropy ratio is less pronounced.
The anisotropy analysis covers multiple Barents Sea areas and includes 20 drilled wells. The wells included in this study have been subdivided in 10 different groups based on their geographical location (Table 1). Note that in area 10 (Hoop) no wells were available, and results are based solely on CSEM data. For each area CSEM data were inverted to determine resistivity and anisotropy values.
The objective of this study is to describe the inequalities of anisotropic rock physics. Anisotropic rock physics provides the link between seismic anisotropy and anisotropic properties of rocks. However, the limitations of anisotropic rock physics predictions and measurements are not well understood. In this study we provided rock physics inequalities as guidelines to check the validities of anisotropic rock physics predictions and lab measurements. Initially we used Rudzki’s inequalities for TI media; then we provided proof of concept of these inequalities as well as extended these inequalities for isotropic media. In addition, we verified these inequalities using published moduli of isotropic crystals, and finally we used these inequalities to check the qualitiy of rock physics predictions and measurements. For spherical pore structure where isotropic self-consistent (SC) rock physics approximations are equal to the anisotropic SC rock physics approximations, inequalities satisfy the rock physics predictions for porosity up-to 60%. With increasing the complexity of pore structure where isotropic rock physics approximations are not equal to anisotropic rock physics approximations, rock physics inequalities describe that part of the anisotropic SC rock physics prediction are not valid for transversely isotropic media. We found these invalid predictions are associated with a higher anisotropic constant. Laboratory measured anisotropic velocity data which have a lower anisotropic constant (less than 0.6) satisfy theses inequalities. However, measured results for clay minerals (e.g. illite and kaolinite) which have a higher anisotropic constant (above 0.6) do not satisfy these inequalities. We concluded these unsatisfied anisotropic rock physics predictions and measurements should be treated as higher anisotropic media (orthorhombic, monoclinic) than transversely isotropic media.
The model uses the differential effective medium models and the probability of In this study a rock physics model is developed to interconnection between inclusions. The level determine the degree of interconnection between idealized interconnection between the fluid inclusion is controlled by fluid inclusions. The method uses a statistical approach to the volume fraction of the fluid and the aspect ratio of the calculate the number of inclusions in a host medium that inclusions. The model is independent of inclusion size and overlap a single inclusion. This degree of interconnection is assumes a statistically random distribution of inclusions.
Seismic and electromagnetic data measure very different physical properties. To exploit these data to their fullest we must understand how each data type is affected by subsurface properties. To this end we have investigated the feasibility of using Controlled-Source Electromagnetic (CSEM) and/or seismic surveying at a sand reservoir in the North Sea. Different geological parameters such as fluid composition, reservoir depth, anisotropy and porosity etc. have been altered to determine which are important and must be considered when considering a survey, designing the survey and/or interpreting the results. Results show that for this site both CSEM and seismic methods could be used and that a combination of the two would be beneficial when interpreting the site. It also shows the value of investigating the effect of changing geophysical parameters. We also develop a joint CSEM and seismic feasibility workflow for the more general scenario.