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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).
Estimation of reservoir properties and monitoring of fluid flow is an important modeling component in hydrocarbon exploration and production. Geophysical data (elastic and electrical) and reservoir rock properties can be linked via rock physics models. In this application, we develop a joint inversion approach of P-wave velocity and resistivity well logs for total porosity and total volume of fluid. We applied our inversion workflow to wireline data measured at the well location of the Rock Springs Uplift, Wyoming, USA, a potential CO2 storage reservoir. The inversion scheme is applied to the actual well dataset and to the same set of well logs filtered at lower resolutions. Results show that the rock and fluid volumes, or at least their trends in the case of filtered data, were predicted by the inversion scheme. Although the method was applied only at the well location, the inversion workflow can be extended to the entire reservoir model to predict the rock and fluid properties of the reservoir away from the well location.
Pan, Haojie (Research Institute of Petroleum Exploration and Development ) | Li, Hongbing (Research Institute of Petroleum Exploration and Development ) | Zhang, Yan (Research Institute of Petroleum Exploration and Development ) | Chen, Jingyi (University of Tulsa) | Cai, Shengjuan (Research Institute of Petroleum Exploration and Development) | Geng, Chao (Petrochina)
Accurate interpretation of the petrophysical properties of gas-hydrate-bearing sediments, such as porosity, hydrate saturation and clay content, are of great importance for reservoir characterization and resource evaluation. Typically, these parameters are estimated using either elastic properties or electrical properties instead of both. We propose to take advantage of multiple types of measurements and improve the accuracy of prediction by using an inverse rock physics modeling (IRPM) method, which allows us to combine elastic and electrical attributes. First, we generate constraint cubes of 3D elastic and electrical data in the reservoir parameter domain using suitable rock physics models calibrated by 3D elasticelectrical rock physics templates (RPTs). Then, we extract the isosurfaces from the 3D elastic and electrical data constraint cubes with the marching-cubes algorithm.
Finally, we use the iterative least-squares method to find the optimal intersection point of three isosurfaces by minimizing the objective function. To demonstrate the feasibility of this strategy, we apply it to synthetic data and well logs measured at the Ocean Drilling Program (ODP) Hole 1247B drilled on the Hydrate Ridge, South Cascadia Margin. For the synthetic data, the estimated Petrophysical properties are consistent with those produced using noise-free initial synthetic model parameters. In addition, our estimated results for real field localities consistently fit with the core data. The smaller root-mean-square errors between inversion results and referenced Petrophysical properties for both synthetic case (≤ 0.06) and real field data (≤ 0.061) further confirm that the inverse rock physics modeling method is feasible for estimating petrophysical properties by integrating elastic and electrical properties.
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 introduce Petrophysical Joint Inversion (PJI) to enhance reservoir characterization through a robust petrophysical model that makes full use of the complementary information contained in multi-physics data such as seismic and Controlled Source ElectroMagnetics (
We show that this multi-physics approach reveals the potential to significantly improve the accuracy with which reservoir properties in general, and saturation in particular, can be determined with resistivity providing a quantitative estimator of commercial HC accumulation trapped in clastic reservoirs.
One of the approach toughest challenges due to the different input data resolution was reconciled applying a ‘localized’ model-based 3D
Through a case study, based on a deep water oil field offshore Brazil, we demonstrate the power of PJI to retrieve the reservoir parameters and augment the certainty with which reservoir lithology and fluid properties are constrained.
Presentation Date: Wednesday, September 27, 2017
Start Time: 3:55 PM
Presentation Type: ORAL