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Abstract In this paper a new approach for joint interpretation of petrophysical measurements acquired by multiple sensors or tools is demonstrated. This approach is based on a mapping function that can be expressed as a linear combination of radial basis functions (RBFs). The theoretical log response equations for the sensors or tools are used to compute a database covering all possible ranges of formation properties, i.e., the solution space. The database is used to construct the mapping function that relates log measurements to formation properties. The solution space can be plotted in 2D or 3D depending on the number of input measurements. The plots provide the petrophysicist with a useful tool to:identify outliers that might denote a model inadequacy, select appropriate equation parameters that might not be obvious from individual logs, and compute a solution consistent with the model equations. The mapping function solutions are computationally fast, unique, and robust. In contrast, traditional multi-tool interpretations based on non-linear minimization techniques often face problems of non-uniqueness and lack of robustness. We demonstrate the new approach using two classic examples. The first is the integration of resistivity and pulsed neutron capture logs to solve simultaneously for water saturations and salinities. This is a non-linear problem that has complex input parameter dependencies. The second example is analysis of thin-beds that have resistivity anisotropy. Even though analytical solutions exist, they are complex and it is difficult to see how, for example, net pay depends on the anisotropy. The choice of these classic examples allows us to show the simplicity of the proposed technique and to benchmark it against the traditional methods.
Natural fractures are often responsible for enhancing production in oil and gas reservoirs. Drill locations are defined from an overlay of three key reservoir attribute maps. Seismic attributes are calibrated to clay content measured in existing well control by wire line logs to define fracture-prone brittle reservoir. Gas sensitive seismic attributes such as the phase gradient (an AVO attribute first developed by GeoSpectrum) are used to define a prospective fairway. Natural fractures are predicted using seismic lineament mapping in the reservoir section. Successful drilling results from 5 new wells indicate the new interpretation method is ready for commercialization, and gas exploration and development.
Multi-component 2D/3D seismic is gaining more and more attention in the E&P world. This seismic methodology is already established as a suitable tool for imaging through gas clouds, for fluid detection and for petrographic characterization. The importance of multi-component surveys relies on the possibility of recording Shear or Converted waves by means of inline and crossline horizontal geophones. Regarding offshore surveys the multi-component methodology is based on Ocean Bottom Cables, a technology that allows the recording of compressional and shear waves directly at the Sea floor. In the recent years Agip performed two OBC acquisitions: a 2D survey in the North Sea, aimed to improve the imaging through gas chimney, and a 3D multi-azimuth, multi-offset survey in the Adriatic Sea aimed to characterize the fracture setting in a carbonatic reservoir. The extra cost of a shear waves acquisition and processing compared to the cost of a standard P wave acquisition is an issue to be carefully considered in evaluating the feasibility of a multi-component project. In some cases the cost can even quadruplicate. There is no doubt that the extra costs would be lowered if shear waves operations were more widely used. The usefulness of multi-component seismic will be discussed through the presentation of the main features of the two Agip surveys and other case histories.
Abdelaal, Atef Farouk (Onshore Oil Operations (ADCO)) | Ihab, Tarek Mohammed (Onshore Oil Operations (ADCO)) | Sayed, Raza Hassan (Onshore Oil Operations (ADCO)) | Murray, Doug (Schlumberger) | Djamila, Benamer (Schlumberger) | Hui, Wang (Schlumberger)