Geo-modelling is usually done to honor static data such as core, well logs and seismic acoustic impedance (AI) map where available. Once the static geo-model is complete, history matching is carried out by tuning the static model properties until the model reproduces observed dynamic behavior. The objective of this paper is to showcase how a systematic a priori integration of dynamic elements into geo-modelling eliminated the need for history matching. These dynamic elements are; connected reservoir regions CRR (
CRRs were defined based on time-lapse shut-in pressure trend groups. Core and log data were grouped on the basis of the identified CRR and used to build CRR-based Neural Network models for predicting permeability logs of non-cored wells within each CRR. The geo-modeler then created two geo-realizations by using the permeability logs within each CRR to distribute permeability within the CRR using two assumptions of variogram lengths (i) variogram range obtained from analysis of limited core data, (ii) variogram range required to ensure intra-CRR connectivity. Pressure transient was simulated for wells with observed PTA data using the two realizations, and a comparison of the log-log plots of simulated pressure transient derivative and observed pressure transient derivative were used to determine the quality of each realization for each well. The realization that provided the least squares of error across all the wells was selected as base-case geo-model. Permeability correction coefficients were applied on the base-case geo-model until PTA kh were acceptably matched. The resulting permeability log at the PTA well is referred to as PTA-corrected permeability log. Some cored wells were originally exempted from the neural-network permeability modelling because they didn't have logs (sonic, density and neutron logs). Hybrid permeability logs were derived from a combination of the predicted permeability logs and core permeability at these well locations.
All permeability correction logs (i) PTA-corrected permeability logs and (ii) Hybrid permeability logs were then fed back into the geo-modeling workflow to generate an improved permeability distribution which respects core data, PTA kh, and CRRs.
The do-nothing simulation run has more than 80% of wells’ pressure data acceptably history matched. This application demonstrates that a priori integration of dynamic elements like CRR, PTA kh, and the use of CCR-based permeability modeling results in a better characterized geo-model with potential for eliminating the need for history matching.
The purpose of this paper is to highlight the similarity between Connected Reservoir Regions (CRR)map created using time-lapse pressure groups (
Time-lapse average reservoir pressure from producers and injectors spanning several years of field production were sorted into groups of similar pressure trends. Wells that show similar pressure trend were classified into same CRR, while wells that show different pressure trends were classified into different CRRs. Only wells operating within the same reservoir zone have been used in the pressure grouping in order to ensure that the observed pressure trend differences are only due to lateral variations of reservoir quality and not due to vertical zonation. A geo-modelling software was used to create connected reservoir regions map in which all wells within the same pressure group are identified with a unique colour code and polygons are drawn to delineate the spatial limits of wells within each pressure group. The CRR map thus obtained, was then compared with SAI map and permeability quality map.
Similarity was observed between the CRR map, SAI map and petro-physical rock quality map. Areas indicated as poor quality (high impedance) on the SAI map and indicated as low permeability on petro-physical map were consistent with CRR regions that are characterized by high injection pressure and poor pressure support. Areas indicated as good quality (low impedance) on SAI map and high permeability on petro-physical rock quality map were consistent with CRR regions that are characterized by low injection pressure and excellent producer-injector communication. In addition, a particular well was sidetracked in order to improve reservoir sweep, this producer whose pressure had been historically fairly steady, experienced a sudden increase of time-lapse average reservoir pressure. When the pre and post sidetrack locations of this well were plotted on CRR map, the reason for the sudden pressure increase became obvious; well was sidetracked across CRR boundary, from a poor reservoir quality to a good reservoir quality CRR.
In certain cases, oil and gas fields may not have seismic data, in other cases the resolution of the returned seismic signal may be weak. In such cases, CRR maps created using time-lapse average reservoir pressure groups could be used during geo-modelling,for controlling the distribution of 3-D properties away from well control points, instead of seismic acoustic impedance reservoir quality map.