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.
San Pedro is a complex reservoir located offshore Bayovar Bay in the province of Piura in northern Peru. Stratigraphically, is located in metamorphic basement
rocks of the basin Sechura, composed of Paleozoic rocks Amotape Group. The San Pedro reservoir corresponds to a naturally fractured reservoir, its structural complexity is linked to tectonic events that led to the formation of different types of fractures and presence of structural forms own metamorfics rock.
Geophysical characterization of the major structural features as faults and fractures, and characterization through special registers such as image registration, sonic dipolar and sonic scanner, conventional records which are taken as inputs for the construction and validation of continuous models petrophysical properties, discrete fracture models and geomechanical models in order to best characterize this reservoir.
Due to the highly complex network of fractures, well productivity is highly variable along the reservoir and is heavily influenced by the prevailing current effort in the field. The productivity of wells due to a directional permeability generates a drainage area of elliptic type which is difficult to define but which is reflected in the
productive performance of wells and gas injection projects that were made in the reservoir. The productivity of the wells is also largely affected by the type of completion used in different parts of the reservoir, the completion type generally used "open hole" which has worked well in some parts of the reservoir but in other not because of the influence of the efforts and the structural forms that it has created.
All these models and production data history help us understand the nature of the dominant effort, the main trends of productive fractures and to identify areas and blocks with potential for further drilling of wells and carry out the development plan the field.
A new workflow reliably characterizes natural fractures in the highly complex Sechura basin in Peru, where acoustic logs alone were unable to characterize fracture networks adequately. The new approach, which integrates the response of borehole images with a dipole sonic tool, directly impacts the decision criteria of producible intervals where the zones that produce are the zones with open fractures aligned with the maximum horizontal stress. A case study of two wells in the Sechura basin shows how the methodology gives a quantitative way to better understand the fracture networks and fracture dynamics in naturally fractured reservoirs, establishing a novel criterion for fast decisions based on colored flags and horizontal stress directions.