Summary The screening of large number of reservoirs for the application of EOR processes has been generally done through "rules of thumb" which often times fail to identify the most suitable reservoirs, due to their binary characteristics, which do not take into account synergistic effects on process performance. Therefore, a new screening process performance. Therefore, a new screening method is developed in this work to rank reservoirs for carbon dioxide flooding which attempts to solve this shortcoming. The method is based on a parametric study, carried out systematically to determine the effect of reservoir properties on reservoir response to the gas injection. The study was done using a fully compositional simulator, a black oil model with a mixing parameter, and a semi-analytical predictive model. Results obtained with the three simulators are presented and compared in this paper. Reservoir parameters examined were temperature, pressure, porosity, permeability, dip, API gravity, oil saturation, net oil sand thickness, minimum miscibility pressure, saturation pressure, remaining oil in place, and reservoir pressure, remaining oil in place, and reservoir depth. The optimum set of parameters which gave the best average oil production rate for a base case was obtained from the simulation studies. The base case consisted of the injection of 2000 MSCF/D of carbon dioxide in a inverted five spot, 40 acres pattern. The decrease in oil production rate with departures of the production rate with departures of the characteristic parameters from the optimum values were also determined to quantify the importance or weight of each property. Actual reservoirs were ranked by an arbitrary heuristic function, called the exponentially varying function, whose value depended exponentially on the weighted differences between the properties, characteristic of the reservoir, and the optimum values obtained from the simulation studies. Results obtained with the three simulators compare quite well and indicate that on the average, the best reservoir for carbon dioxide injection should have an oil gravity of 36 degrees API, a temperature of 150 degrees F, a permeability of 300 mD, an oil saturation at the start of the injection of 60 %, a reservoir pressure at the time of injection of around 200 psi over minimum miscibility pressure, a porosity of 20 %, a net sand thickness of 40 ft and a reservoir dip of 20 deg. Of the above parameters, those whose changes around the optimum influence the most process performance are API gravity, oil process performance are API gravity, oil saturation and reservoir pressure. Therefore, the reservoirs with these three parameters closer to the optimum values are the best candidates for CO injection. This is taken adequately by the exponentially varying function defined in the paper. The procedure was applied to rank about six hundred reservoirs in the greater Anaco and Oficina areas of Eastern Venezuela in order to identify the most suitables for a pilot test. The reservoir ranked 30th by the method hereby described was chosen to implement the pilot. This procedure could be easily extended to other EOR processes once the necessary simulations are carried out.

The multiattribute rotation scheme (MARS) is a methodology that uses a numerical solution to estimate a transform to predict 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 Euclidean space formed by an n number of attributes and subsequent scaling of this attribute to the target unit properties. We have computed the transform from well-log-derived elastic attributes and petrophysical properties, and we have posteriorly applied it to seismically derived elastic attributes. Such transforms can be used to estimate reservoir property volumes for reservoir characterization and delineation in exploration and production settings and to estimate secondary variables in geostatistical workflows for static model generation and reserve estimation. To illustrate the methodology, we applied MARS to estimate a transform to predict the water saturation and total porosity from elastic attributes in a well located in the Barents Sea as well as to estimate a water-saturation volume in a mud-rich turbidite gas reservoir located onshore Colombia.

The inherent nonuniqueness of geophysical analysis can mean that interpretations based only on a single geophysical measurement can be ambiguous or uncertain. We have developed a case study from the Hoop area of the Barents Sea, in which prestack seismic, well-log, and controlled-source electromagnetic (CSEM) data were integrated within a rock-physics framework to provide a more robust assessment of the prospectivity of the area than could be obtained by seismic analysis alone. In this example, although quantitative seismic interpretation identified potentially hydrocarbon-bearing sands, the saturation was uncertain. In this area and at shallow depths, the main focus is on (very) high oil saturations. Adding the CSEM data in this setting allows us to distinguish between high saturations ([Formula: see text]) and low and medium saturations ([Formula: see text]): It is clear that saturations similar to those observed at the nearby Wisting well ([Formula: see text]) are not present in this area. However, because of limitations on the sensitivity 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. Based on the resulting downgrade of the main prospect Maya and the limited additional high-risk prospectivity at other stratigraphic levels, the partnership agreed to surrender the license.

We have developed an example from the Hoop Area of the Barents Sea showing a sequential quantitative integration approach to integrate seismic and controlled-source electromagnetic (CSEM) attributes using a rock-physics framework. The example illustrates a workflow to address the challenges of multiphysics and multiscale data integration for reservoir characterization purposes. A data set consisting of 2D GeoStreamer seismic and towed streamer electromagnetic data that were acquired concurrently in 2015 by PGS provide the surface geophysical measurements that we used. Two wells in the area — Wisting Central (7324/8-1) and Wisting Alternative (7324/7-1S) — provide calibration for the rock-physics modeling and the quantitative integrated analysis. In the first stage of the analysis, we invert prestack seismic and CSEM data separately for impedance and anisotropic resistivity, respectively. We then apply the multi-attribute rotation scheme (MARS) to estimate rock properties from seismic data. This analysis verified that the seismic data alone cannot distinguish between commercial and noncommercial hydrocarbon saturation. Therefore, in the final stage of the analysis, we invert the seismic and CSEM-derived properties within a rock-physics framework. The inclusion of the CSEM-derived resistivity information within the inversion approach allows for the separation of these two possible scenarios. Results reveal excellent correlation with known well outcomes. The integration of seismic, CSEM, and well data predicts very high hydrocarbon saturations at Wisting Central and no significant saturation at Wisting Alternative, consistent with the findings of each well. Two further wells were drilled in the area and used as blind tests in this case: The slightly lower saturation predicted at Hanssen (7324/7-2) is related to 3D effects in the CSEM data, but the positive outcome of the well is correctly predicted. At Bjaaland (7324/8-2), although the seismic indications are good, the integrated interpretation result predicts correctly that this well was unsuccessful.

The Plato Depression in the Lower Magdalena Basin is a Miocene depocenter where a thick, shale-prone marine sequence known as the Porquero Formation was laid down in basin-floor conditions. Seismic inversion carried out on new and existing 2D seismic data helped to focus early exploration on a shallow stratigraphic gas-sand play associated with what seemed to be isolated shale diapirs with shallow roots. A subsequent land 3D survey helped to locate the first exploratory well, which resulted in the discovery of the Guama gas-condensate field. The main reservoir consists of laminar, low-permeability sands in a relatively thick shale-prone sequence of Early and Middle Miocene age. Sequential application of acoustic and elastic inversion and AVO analysis was used to build an evolving 3D predictive model of gas sands, extracted from an otherwise featureless seismic cube. Workflows were based on careful rock-physics analysis, simultaneous seismic inversion, and AVA analysis supported by custom well-log and seismic-gather conditioning. Work routines carried out in parallel became essential to applying quality control and fine-tuning the model, which supported three additional successful wells, early reservoir planning, and key volumetrics.