Alvarez, Pedro (Rock Solid Images) | Marin, William (Rock Solid Images) | Berrizbeitia, Juan (Rock Solid Images) | Newton, Paola (Rock Solid Images) | Bolivar, Francisco (Rock Solid Images) | Barrett, Michael (African Petroleum Corporation) | Wood, Harry (African Petroleum Corporation)
We have evaluated a case study, in which a class-1 amplitude variation with offset (AVO) turbiditic system located offshore Cote d’Ivoire, West Africa, is characterized in terms of rock properties (lithology, porosity, and fluid content) and stratigraphic elements using well-log and prestack seismic data. The methodology applied involves (1) the conditioning and modeling of well-log data to several plausible geologic scenarios at the prospect location, (2) the conditioning and inversion of prestack seismic data for P- and S-wave impedance estimation, and (3) the quantitative estimation of rock property volumes and their geologic interpretation. The approaches used for the quantitative interpretation of these rock properties were the multiattribute rotation scheme for lithology and porosity characterization and a Bayesian litho-fluid facies classification (statistical rock physics) for a probabilistic evaluation of fluid content. The result indicates how the application and integration of these different AVO- and rock-physics-based reservoir characterization workflows help us to understand key geologic stratigraphic elements of the architecture of the turbidite system and its static petrophysical characteristics (e.g., lithology, porosity, and net sand thickness). Furthermore, we found out how to quantify and interpret the risk related to the probability of finding hydrocarbon in a class-1 AVO setting using seismically derived elastic attributes, which are characterized by having a small level of sensitivity to changes in fluid saturation
Presentation Date: Tuesday, October 16, 2018
Start Time: 1:50:00 PM
Location: 209A (Anaheim Convention Center)
Presentation Type: Oral
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.
Summary This paper presents a workflow to estimate brittleness, porosity, and total organic carbon from elastic attributes. The estimation is carried out through the application of the multi-attribute rotation scheme. This method is a hybrid rock-physics/statistical approach that uses a global search algorithm to estimate a customized transform for each geologic setting in order to predict petrophysical properties from elastic attributes. After the application of this technique, customized transforms were derived for the analyzed geological setting, to estimate porosity, brittleness, total organic carbon and litho facies logs from elastic attributes. The final goal of this workflow is to apply these transforms over seismically-derived attributes to generate volumes of these petrophysical properties that can be used for reservoir characterization and production optimization.
We present a case study from offshore north-west Australia where two different workflows to quantitatively interpret seismic inversion attributes based on a rock physics and statistical framework were applied and compared. The first method, statistical rock physics, used a supervised facies classification of the seismic inversion attributes based on a Bayesian approach, using well log information as training data. From this workflow, the probability of occurrence of each facies and a volume of the most likely litho-fluid facies were estimated. These are fundamental tools used to characterize the location of pay facies with a corresponding uncertainty. The second method, the multi-attribute rotation scheme, uses a global search algorithm to estimate an optimal transform between elastic attributes and a target petrophysical property. The target property used in this case study was the hydrocarbon pore volume of the rock. This is a key rock property that combines information on the lithology, porosity and fluid saturation of the rock, and has the advantage that when spatially integrated provides information about the total volume of hydrocarbon in the reservoir. The result obtained from the different workflows are consistent and illustrate the advantages that a careful seismic reservoir characterization study can provide for the exploration, appraisal and production of hydrocarbon.
Presentation Date: Tuesday, September 26, 2017
Start Time: 2:40 PM
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
The multi-attribute rotation scheme (MARS) is a methodology that uses a numerical solution to estimate a transform to estimate petrophysical properties from elastic attributes. This is achieved by estimating a new attribute in the direction of maximum change of a target property in an n-dimensional Euclidian space formed by n attributes, and subsequent scaling of this attribute to the target unit properties. This approach is performed using well log-derived elastic attributes and petrophysical properties, and posteriorly applied over seismically-derived elastic attributes. In this study MARS was applied to predict a transform to estimate water saturation and total porosity from elastic attributes, using a two- and three-dimensional approach, respectively. The final goal of this workflow is to apply these transforms over seismically-derived attributes to generate volumes of these properties, which can be used in exploration and production settings for reservoir characterization and delineation, as well as soft variables in geostatistical workflows for static model generation and reserve estimation.
A common way to understand the relationship between seismic attributes and a petrophysical property is by the use of rock physics templates or simply by cross-plotting well log derived elastic attributes color-coded by a petrophysical property. Both ways graphically illustrate the relationship between the elastic and petrophysical domains, which can be used to estimate reservoir properties from seismic inversion attributes. MARS is a methodology that uses a numerical solution to estimate a mathematical expression that reproduces the aforementioned phenomena. This methodology uses, as input, measured and/or rock physics-modelled well log information, to estimate a well log-derived transform between several elastic attributes and target petrophysical properties. The objective of this workflow is to apply the resultant transform over seismically-derived elastic attributes to predict the spatial distribution of petrophysical reservoir properties.
Theory & Method
MARS estimates a new attribute (τ) in the direction of maximum change of a target property in a n-dimensional Euclidian space formed by n attributes. We search for the maximum correlation between the target property and all the possible attributes that can be estimated via axis rotation of the basis that forms the aforementioned space.