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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 carry out the two-dimensional inversion of marine controlled-source electromagnetic data from the SEG advance modeling program using MARE2DEM Software.We applied this inversion on three survey lines from the given data set to image the salt body and delineate thin hydrocarbon reservoirs that are present near the salt flanks.The inversion was unconstrained and did not use any a priori information about the salt body from the seismic imaging or nearby well logs. Despite the complex 3D structure of thesalt model, our inverted results agree well with the truemodel demonstrating the robustness of the method in imaging the reservoirs and their lateral extents without any prior information.
Presentation Date: Tuesday, October 16, 2018
Start Time: 9:20:00 AM
Location: Poster Station 15
Presentation Type: Poster
We carry out the inversion of marine controlled-source electromagnetic data using real coded genetic algorithm to estimate the isotropic resistivity. Unlike linearized inversion methods, genetic algorithms belonging to class of stochastic methods are not limited by the requirement of the good starting models. The objective function to be optimized contains data misfit and model roughness. The regularization weight is used as a temperature like annealing parameter. This inversion is cast into a Bayesian framework where the prior distribution of the model parameters is combined with the physics of the forward problem to estimate the aposteriori probability density function in the model space. The probability distribution derived with this approach can be used to quantify the uncertainty in the estimation of vertical resistivity profile. We apply our inversion scheme on three synthetic data sets generated from horizontally stratified earth models. For all cases, our inversion estimated the resistivity to a reasonable accuracy. The results obtained from this inversion can serve as starting models for linearized/higher dimensional inversion.
Presentation Date: Monday, October 15, 2018
Start Time: 1:50:00 PM
Location: Poster Station 13
Presentation Type: Poster
Alvarez, Pedro (RSI) | Marcy, Fanny (Engie) | Vrijlandt, Mark (Engie) | Nichols, Kim (RSI) | Keirstead, Rob (RSI) | Smith, Maggie (RSI) | Wen Tseng, Hung (RSI) | Bouchrara, Slim (RSI) | Bolivar, Francisco (RSI) | Rappke, Jochen (Engie) | MacGregor, Lucy (RSI)
We present a case study from the Hoop area of the Barents Sea, in which seismic, well log and controlled source electromagnetic (CSEM) data were integrated within a rock physics framework, to provide a robust assessment of the prospectivity of the area. Combining seismic and CSEM results can resolve the ambiguities that are present when only a single data type is considered. In this example, although seismic data identified potential hydrocarbon bearing sands, the saturation was uncertain. In this area and at shallow depth, the main focus is on (very) high oil saturations. Adding the CSEM data in this setting allows us to distinguish between high saturations (> 70%), and low and medium saturations (< 50%): it is clear that saturations similar to those observed at the nearby Wisting well (>90%) are not present in this area. However, because of limitations on the sensitivity/recoverability of the CSEM data in this high resistivity environment, it is not possible to distinguish between low and medium saturations. This remains an uncertainty in the analysis.
Presentation Date: Wednesday, September 27, 2017
Start Time: 3:30 PM
Presentation Type: ORAL
We carry out inversion of the marine controlled-source electromagnetic data using genetic algorithm to estimate the subsurface vertical resistivity. This inversion is cast into a Bayesian framework where the prior distribution of the model parameters is combined with the physics of the forward problem to estimate the a-posteriori probability density function in the model space. The probability distribution derived with this approach can be used to quantify the uncertainty in the estimation of vertical resistivity profile. We apply our inversion scheme on two synthetic data sets generated from two different horizontally stratified earth models. The first model had one thin resistive hydrocarbon layer between the low-resistive sediments, whereas the second model had multiple thin resistive layers. For both cases, our inversion estimated the resistivity to a reasonable accuracy. Additionally, we tested our method to invert the multi-frequency data which further improved the quality of the inverted results. The results obtained from this inversion can form a basis for higher dimensional modelling and inversions. Also, this method can be easily extended to implement the joint inversion using seismic data.
Presentation Date: Wednesday, September 27, 2017
Start Time: 2:40 PM
Location: Exhibit Hall C/D
Presentation Type: POSTER
Estimation of reservoir rock and fluid properties is highly dependent on the parameters used in the rock physics models. Determining water saturation is very important to estimate hydrocarbon reserves and is often computed using Archie’s equation. Any uncertainty associated with the parameters in the Archie model as well as resistivity of clay in shaly sand formations may cause errors in the estimation of petrophysical parameters. It is very important to evaluate the relative impact of these parameters on reservoir rock and fluid properties. In this work, a series of sensitivity tests were carried out in order to investigate which of the formation water resistivity
Presentation Date: Wednesday, October 19, 2016
Start Time: 2:45:00 PM
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.
Using a subset of the SEG SEAM controlled source electromagnetic (CSEM) data, we attempted to improve the The client set the company a task: based on the acquired interpretation workflows in a blind test using various CSEM data, two well log suites and two seismically available software and interpretation approaches. To avoid defined horizons, provide interpreted results to address two bias introduced by any approach due to its inherent and questions: technical limitations, we started with feasibility study to 1) What is the extent of the hydrocarbon encountered establish the recoverability of the prospect and carefully in one of the wells?
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.