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.
Tosi, Gianbattista (Eni) | Stensland, Dag (Eni) | Winterton, David (Eni) | Zappalorto, Luigi (Eni) | Wang, Haifeng (Schlumberger) | Dupuis, Christophe (Schlumberger) | Denichou, Jean-Michel (Schlumberger) | Horstmann, Mathias (Schlumberger)
Goliat was the first oil discovery in the Barents Sea and holds 174 million barrels of recoverable oil. It is operated by Eni Norge (65% share, with Statoil holding the rest of the equity). Finding oil is important, but safely and efficiently developing it is critical. The remoteness of these frontier projects amplifies the consequences of any delays or issues. Risk prevention and mitigation is the object of particular attention in this new frontier area. Landing the first producer was a particularly sensitive operation, requiring a safe stop a few meters above the top of the reservoir to reduce the risk of landing the section within the reservoir gas cap bearing sandstone.
A geometrical landing would be limited by a depth uncertainty of more than 10 m true vertical depth (TVD): relying on surface seismic information is subject to seismic time-to-depth conversion and the inherent limited resolution of seismic data. Taking these uncertainties into account normally requires setting the casing long in advance of the reservoir top, giving way to a much longer portion of the reservoir section exposed to the overburden shales, with consequent 8 ½’’well bore instability issues and risk of plugging the completion screens.
For the Goliat well, the operator adopted a different approach to land the objective while preventing the risks of setting casing too soon or too late. A new ambitious objective was set: stopping and casing as close as possible to the reservoir, but no closer than 5 m TVD. This could only be done by using the latest generation of deep directional resistivity (DDR) logging-while-drilling tools in the 12 ¼" section, increasing both the precision and the accuracy of the landing by relying on a direct detection of the reservoir top before drilling into it.
The DDR real-time automatic inversion of the subsurface layering revealed the top of the reservoir from 19 m TVD below the bottom hole assembly, a new record. By tracking the top boundary, even at a steep inclination near 70°, the operator confidently stopped drilling when the bit was 6 m TVD above the top of the reservoir, as planned, safely minimizing the distance to be drilled in shales before intersecting the reservoir. The distance to the reservoir was verified in the next section drilled.
The use of DDR for landing wells accurately either above or just below a top reservoir is now a proven powerful option for drilling programs in which risk prevention is required at the top of the reservoir.
Permeability provides a measure of the ability of a porous medium to transmit fluid and is significant in evaluating reservoir productivity. A case study that compares different methods of permeability prediction in a complex carbonate reservoir is presented in this paper. Presence of siliciclastic fines and diagenetic minerals (e.g., dolomite) within carbonate breccias has resulted in a tight and heterogeneous carbonate reservoir in this case. Permeability estimations from different methods are discussed and compared. In the first part of the paper, permeability measurements from conventional core analysis (CCAL), mercury-injection capillary pressure (MICP) tests, modular formation dynamic tests (MDTs), and nuclear-magnetic-resonance (NMR) logs are discussed. Different combinations of methods can be helpful in permeability calculation, but depending on the nature and scale of each method, permeability assessment in heterogeneous reservoirs is a considerable challenge. Among these methods, the NMR log provides the most continuous permeability prediction. In the second part of the paper, the measured individual permeabilities are combined and calibrated with the NMR-derived permeability. The conventional NMR-based free-fluid (Timur-Coates) model is used to compute the permeability. The NMRestimated permeability is influenced by wettability effects, presence of isolated pores, and residual oil in the invaded zone. new modified Timur-Coates model is established on the basis of fluid saturations and isolated pore volumes (PV) of the rock. This model yields a reasonable correlation with the scaled core-derived permeabilities. However, because of the reservoir heterogeneity, particularly in the brecciated intervals, discrepancies between the core data and the modified permeability model are expected.