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In this paper we describe the application of high-resolution 3D CSEM and seismic data for a shallow exploration target called Gemini N close to the Wisting discovery in the Barents Sea. Like Wisting, Gemini N also exhibits coincident CSEM and seismic anomalies that support the presence of high-saturation hydrocarbon reservoir, but the situations in the two data domains are still very different. Compared to Wisting, the seismic anomaly at Gemini N is much stronger, while the CSEM anomaly is much weaker. The shallow target burial depth combined with a high resistivity background was an ideal setting to deploy high-resolution CSEM. Feasibility modeling showed that CSEM frequencies as high as ∼50 Hz with a 1 km receiver and 500 m towline spacing would be effective. A new acquisition led to results that were revealing and realized much higher resolution and sensitivity to the buried resistor properties. For seismic, a 3D ultra-high resolution (P-Cable) dataset was already available over Gemini N. These data provided vital details to the reservoir geometry and were very important for the joint seismic and CSEM interpretation of Gemini N. Our pre-drill prediction came out accurate for thickness of pay in good reservoir. There was still a caveat, though. Unlike Wisting this one was gas which is still a challenge in much of the Barents Sea.
Presentation Date: Thursday, October 18, 2018
Start Time: 8:30:00 AM
Location: 213A (Anaheim Convention Center)
Presentation Type: Oral
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
In this paper we describe the highlights from a wide range of CSEM applications and developments in the Wisting area. At an initial stage, by including higher frequencies in 3D CSEM inversion at Wisting, we realized that our CSEM data contained a lot more detailed information about reservoir properties than earlier anticipated. Beyond the traditional application of predicting high vs. low hydrocarbon saturation, the CSEM data are used for estimation of reservoir heterogeneity and even connectivity. Our quantitative workflows are still maturing and are expected to provide future value. At Wisting we have been fortunate to be in an active appraisal setting where new wells have repeatedly provided calibration and adjustment to our CSEM workflows. During almost four years we have acquired two field-scale tailored 3D CSEM surveys with gradually denser spatial sampling and higher frequencies. These have provided higher accuracy and better spatial resolution than the conventional coarse-grid survey design used in multi-client projects. Our project work has been highly cross-disciplinary, where CSEM expertise paired with specialists in rock physics, seismic AVO and geology has worked very well. Our ability to operate as one team across company barriers is a key success factor with learning, re-learning and geoscience integration as main ingredients.
Presentation Date: Thursday, September 28, 2017
Start Time: 8:30 AM
Presentation Type: ORAL
ABSTRACT We have developed a workflow for quantitative interpretation of CSEM data. The interpretation method utilizes that CSEM responses are determined by the product of resistivity-contrast and thickness for hydrocarbon reservoirs and resistive lithologies. This enables exhaustive investigation of rock physics models supporting the measurements, and uncertainty estimation in the property domain. The properties that can be estimated are e.g. hydrocarbon volume and gross thickness of the hydrocarbon saturated column. The property and column predictions are on the reservoir scale and can be directly evaluated in conjunction with thickness estimates from seismic. Our approach is also suited for evaluation of anti-models as the cause for a resistor. This analysis can support interpretation of a resistor mapped using CSEM and give input to risking. We illustrate the workflow and discuss resistor causation using Barents Sea examples and compare predictions to drilling results for a fluid case and a tight-reservoir case. Presentation Date: Tuesday, September 17, 2019 Session Start Time: 8:30 AM Presentation Time: 8:30 AM Location: 225C Presentation Type: Oral
Seismic reservoir characterization aims to provide an accurate reservoir description of rock and fluid properties estimated from seismic data. However, in several applications, seismic data only, cannot accurately discriminate the fluid effect, and the integration of other geophysical measurements, such as electromagnetic data, is required to improve the reservoir description. In this work, we propose a joint rock physics inversion to estimate porosity and fluid saturations from seismic velocity and electrical resistivity. The method is based on a Bayesian approach to inverse modeling and combines inverse theory and statistical rock physics relations. The advantages of this approach are the joint estimation of rock properties, achieved by a coupled rock physics model, and the estimation of the uncertainty associated to the predicted model, achieved through the Bayesian approach. The method has been applied to a real dataset, the Rock Spring Uplift field in Wyoming, a CO2 sequestration study.
The goal of seismic reservoir characterization is to provide a reliable model of the reservoir, in terms of rock properties, such as porosity and lithology, and fluid saturations. In rock physics models, when rock properties are known, we can predict the effect of fluid saturations on P-wave and S-wave velocity and density (Mavko et al., 2009; and Dvorkin et al., 2014). However, the solution of the inverse problem, i.e. the estimation of rock and fluid properties from velocities and density, is generally a challenging task (Avseth et al., 2005; and Doyen, 2007). Indeed, the solution of the inverse problem is not necessarily unique: two different rocks could have different porosities, lithologies and fluids, and the same elastic response. Furthermore, when the inverse problem is solved using seismic data instead of well log data, the low resolution and low signal-to-noise ratio of the data often increase the uncertainty in the estimation of seismic velocities and density, which makes the rock-fluid property estimation more challenging. To improve the reservoir description and reduce the associated uncertainty, we propose to integrate electromagnetic (EM) data, together with seismic attributes, in the reservoir modeling workflow (Du and MacGregor, 2010; MacGregor, 2012).