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Collaborating Authors
Gilbert, Fabien
Foothills structural model de-risking with 3D magnetotellurics
Miorelli, Federico (CGG) | Mackie, Randall L. (CGG) | Gilbert, Fabien (TOTAL) | Soyer, Wolfgang (CGG)
ABSTRACT Geophysical imaging in the foothills environment is typically hampered by complex structures, and the high cost of data acquisition in poorly accessible, rugged topography. Seismic imaging is particularly difficult due to poor signal penetration, steeply dipping structures and irregular data coverage. The use of magnetotellurics (MT) has become a successful complementary tool, due to good sensitivity to the deep resistive targets typically encountered below folded sequences of more conductive units. Due to non-uniqueness and resolution limitations, MT 3D inversion requires additional constraints in order to recover a reliable image. These usually come from geological interpretation of available seismic and well data; however it is often the case that several competing structural models can be derived. We employ a ranking workflow that uses MT inversion to assess, via a cross-gradient operator, whether the structural models are compatible with the MT observations. We further apply 3D non-linear uncertainty estimation to address the reliability of the inversion results, obtaining a bounding envelope of the resistive anomaly. Presentation Date: Wednesday, September 18, 2019 Session Start Time: 8:30 AM Presentation Time: 8:30 AM Location: 225C Presentation Type: Oral
- Geophysics > Electromagnetic Surveying (1.00)
- Geophysics > Seismic Surveying > Seismic Processing (0.54)
- South America > Bolivia > Santa Cruz Department > Santa Cruz Basin > Ipati Block > Incahuasi Field (0.99)
- South America > Bolivia > Santa Cruz Department > Santa Cruz Basin > Aquio Block > Incahuasi Field (0.99)
ABSTRACT Total EP Bolivie carried out two Magnetotellurics (MT) surveys in the bolivian fold belt of the sub-andean foothills. These surveys were aiming to estimate the local absolute depth of the Los Monos/Huamampampa (Devonian) resistivity interface at depth about 5–6 km, and map the relative geometry of this resistivity interface. To achieve this goal, MT was extensively analyzed with a dedicated workflow. First, data quality was assessed; then, the most up-to-date geological interpretation was introduced in sequential 2D/3D unconstrained & constrained inversions; the last step was the updated HMP interpretation from the results of 2D and 3D inversions. Presentation Date: Thursday, October 18, 2018 Start Time: 8:30:00 AM Location: 213A (Anaheim Convention Center) Presentation Type: Oral
- South America > Bolivia > Santa Cruz Department > Santa Cruz Basin > Ipati Block > Incahuasi Field (0.99)
- South America > Bolivia > Santa Cruz Department > Santa Cruz Basin > Aquio Block > Incahuasi Field (0.99)
- Africa > South Africa > Western Cape Province > Indian Ocean > Bredasdorp Basin > Block 9 > EM Field (0.99)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (0.70)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Cross-well tomography (0.55)
- Reservoir Description and Dynamics > Reservoir Characterization > Exploration, development, structural geology (0.50)
Figure 1 gives an Acquisition of appropriate and fit to purpose example of an integrated workflow with the geophysical data can reduce exploration risk and improve efficiency. If there is a real strategy behind the acquisition and integration of multi-geophysical data, a less uncertain earth model can be produced. Two case studies illustrate the value of acquisition and integration of non-seismic data for two different proposes. Gravity and Magnetic data when combined with seismic helps constrain the position of salt bodies and massive volcanic features and is helpful for sub-salt interpretation. Resistivity (MT) information when used with seismic helps resolve land seismic imaging problems especially for the shallower parts of the section in a complex setting.
- Geology > Geological Subdiscipline (0.69)
- Geology > Rock Type > Sedimentary Rock (0.48)
- Geophysics > Magnetic Surveying (1.00)
- Geophysics > Seismic Surveying > Seismic Processing (0.92)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling (0.72)
- Geophysics > Seismic Surveying > Surface Seismic Acquisition (0.68)
Summary We present a methodology which generates equiprobable images of a reservoir property, constrained both by the well and the seismic data, and taking fully into account the scale difference between both data. Our methodology is based on a spectral decomposition of high resolution well information using spectral characteristics of seismic attributes. Different frequency components are simulated by geostatistical techniques with seismic information as a constraint for the simulation of the component which bandpass is consistent between well and seismic data. All simulated frequency components are then recombined and transformed to generate the desired reservoir property. Introduction Modeling the spatial distribution of reservoir properties, such as porosity or permeability, is essential for reserves assessment, flow simulation or reservoir monitoring.
Abstract The spatial heterogeneity of rock properties within a hydrocarbon reservoir has a large effect on fluid flow. Reservoir heterogeneity can be described over a wide range of spatial scales. Each data source that is integrated into the geological model represents a specific scale of information. For example, well data generally provide finer-scale information than do seismic data. The proper integration of different data types into the geological model should account for their difference of scale. Recently, several geostatistical methods have been developed to account for variation in different scales of heterogeneity. Deficiencies in those methods include an inability to identify spatial components that have physical interpretations (factorial kriging), a difficulty in controlling model perturbations (sequential gaussian simulation with non stationary kriging or block cokriging). These deficiencies are particularly true when integrating seismic information into the geological model. In this context, we propose a methodology which allows integration of reservoir properties (known at wells, with high vertical resolution) and seismic attributes (laterally dense but with low vertical resolution) overcoming these deficiencies. Our methodology is based on a spectral decomposition of high resolution well information using spectral characteristics of seismic attributes. Different components are then simulated with seismic information as a constraint for the component which bandpass is consistent between well and seismic data. All components are then recombined and transformed to generate the desired reservoir property. This methodology is evaluated against others in terms of easiness of implementation, computing time, impact of the seismic data and similarity to a synthetic model. An application of this methodology to a real data set is presented. The geological environment of the reservoir is a mixed carbonate platform. Porosities are simulated at a fine scale constrained by a 3D impedance cube. Introduction Modeling the spatial distribution of reservoir properties, such as porosity or permeability, is essential for reserves assessment, flow simulation or reservoir monitoring. To reduce the uncertainty on reservoir property between wells and to ensure data consistency, all relevant information should be taken into account in reservoir modeling. For this reason, stochastic simulations of a reservoir property (known at wells, laterally sparse, with high vertical resolution) commonly integrate seismic data (provided on 2D or 3D grids, laterally dense, but with low vertical resolution) as "soft" secondary information. Still, the problem of scale (or resolution) difference between well log data and seismic data (of a much larger volume support) should be addressed (Fig. 1). Authors consider often only one specific 2D seismic map extracted from the seismic dataset (generally a seismic attribute map at the top or bottom of the reservoir under study), which they relate to the vertically averaged reservoir property. For example, Behrens et al., Yao and Journel, and Doyen et al. proposed a two-step methodology; in the first step, a 2D seismic-derived reservoir property average map is estimated; in the second step, this estimated map is used as a constraint in the simulation of the 3D reservoir property cube (at quasi-point scale). Deutsch et al. proposed a more straightforward approach for modelling a 3D reservoir property cube, based on the simulated annealing technique: a 3D reference image of the reservoir property is iteratively re-arranged according to a user-defined objective function. However, all these approaches, based on the integration of one 2D seismic map, underlie that the seismic volume support is 1D vertical. This raises two concerns; in case of an actual seismic resolution greater than the reservoir thickness, part of the seismic information is not used; in the opposite case, correlating one specific seismic map to the vertically averaged reservoir property is not very accurate.
- Geology > Geological Subdiscipline (0.87)
- Geology > Sedimentary Geology > Depositional Environment (0.66)
- Geology > Rock Type > Sedimentary Rock > Carbonate Rock (0.48)
Summary We present a method that combines seismic stratigraphic inversion and stochastic modeling techniques to generate reliable fine-scale impedance models with associated uncertainties. The seismic stratigraphic inversion technique provides an optimal acoustic impedance model at seismic scale consistent with the seismic data, the low frequency (filtered) well data and geological information. Then stochastic modeling techniques allow the generation of alternative realizations of fine-scale impedance models consistent with the optimal seismic scale impedance model, conditioned to high frequency well data and taking into account the scale difference between both information. Realizations filtered at different frequencies (i.e. different scales) are analyzed in term of uncertainties. It shows the strong impact of high frequency components of well data on the variability of the realizations and the necessity of constraining the very low frequency component of the acoustic impedance inverted model very carefully.