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Summary

Reservoir simulation has entered a new era with the advent of massively parallel processing (MPP) and its ability to handle millions of cells. In this study, a simulation model was constructed from a geologic model without scaleup, with the resulting model having 1.38 million cells. The majority of the effort in reservoir simulation was spent on reviewing geologic and engineering data. History-match changes were made at the well level and interpolated in k_H and k_V arrays with a deterministic mapping routine. Despite the complexity, the model size, and 30 years of history, a successful history match was achieved after 40 runs. The model has been used to identify well locations for bypassed oil recovery. To date, these locations have not been drilled.

Introduction

Reservoir simulation projects are often a balance between the complexity of (presumably) producing more accurate results and reasonable computer turnaround time. Because of computer resource constraints, conventional simulation technology usually requires upscaling of high-resolution geology. Recent developments in MPP computer technology have dramatically increased computer horsepower, giving us the choice of either enhancing geologic and/or engineering complexity or enjoying faster turnaround.^1-3

Is more model detail worth it? Will it better replicate the physics in the reservoir? Will we be able to make better decisions with a high-resolution model? In this case study, the Hadriya reservoir in Berri field was chosen to test this concept and to act as a test case for Saudi Aramco's new POWERS MPP simulator.⁴

Background

Berri Hadriya has been on production since 1970 and on peripheral waterflood since 1975. Water encroachment into the reservoir is very complex, with extensive water-over-oil bypassed areas. Over the years, a comprehensive well-logging program has monitored this water encroachment. Remaining development options are mainly in bypassed areas behind the flood front or in dry areas of lower rock quality. The overall objective of this project was to guide development drilling, especially in the bypassed oil areas.

The Berri Hadriya reservoir is characterized by highly permeable intervals separated by thin, tight streaks that influence water encroachment. To maintain reservoir character and capture the complex water encroachment, the decision was made to simulate the reservoir at the scale of the geologic model. The resulting simulation model has 1.38 million cells.

During the history match, only the permeability was changed; structure, cell thickness, porosity, and facies from the geologic model remained the same. The overall strategy was to make changes only at well locations and to use a mapping program to rebuild the model for each run. Where available, core data, pressure buildups, and flowmeters would be honored.

Geology

The Hadriya reservoir in Berri field is a north/south-oriented wedge-shaped accumulation of grainy carbonates deposited in a distally steepened outer-ramp environment.⁵ Facies patterns developed in broad east/west-oriented bands that grade from algal grainstones in the north to lime mudstones in the south. Interbedded fine- to coarse-grained skeletal grainstones comprise the bulk of the reservoir. Marine cementation is pervasive, with the top of each parasequence marked by a porosity minimum. In addition to this bed-parallel marine cementation, there is another major diagenetic phenomenon in the reservoir. A cross-cutting microporous zone dominates the northeast portion of the field. Overall, the microporosity and associated intergranular cementation are porosity- conservative, but permeability-destroying, processes. Thirty-eight geologic layers were picked based on porosity and gamma ray logs and core data.

The original geologic model included internal pinchouts within the structural framework. To accommodate POWERS' limitations, the geologic model was revised to avoid internal pinchouts by combining sequences. The resulting geologic model has 2.6 million cells (128×160×128) comprising 25 sequences, 14 facies, and 128 layers with a 250-m (820-ft) grid. A 3D structure map with the geologic model grid is shown in Fig. 1. Horizontal permeability values were calculated from five facies-specific porosity/ permeability transform groups.

Well Data

Before constructing the simulation model, a set of personal computer (PC) spreadsheets was created for every well. These spreadsheets were used to organize and review geologic and engineering data and to calculate model parameters, as well as to link changes at the well level with the simulation model.

The spreadsheets were first used to compare well data with the contents of the geologic model at well locations. Fig. 2 shows log porosity with the spreadsheet value plotted on the same scale. Geologic sequences are color-coded green for permeable facies and red for barrier facies. Core data also were included with the well model, depth-shifted to correspond with log porosity. Subsequently, core permeability was used to develop the J-curves used in history matching.

Flowmeter data and pressure-buildup results were incorporated into the spreadsheets. Fig. 3 shows an example of flowmeter results compared with model-normalized cumulative permeability-thickness values. Many of the buildup tests were done on wells with water production. Because the presence of water drastically reduces total mobility, the test results needed to be corrected. Al-Khalifa et al.^6-8 introduced concepts (which were used in this study) to compute corrected buildup permeability in the presence of water. For buildups affected by water, permeability was adjusted with flowmeter data to quantify the fractional flow of water by zone.

Horizontal permeability values in the geologic model were calculated from facies-specific transforms. Within the PC spreadsheets, multipliers for each layer were introduced to adjust the original transform permeability to replicate applicable pressure buildup, core, and flowmeter data. Thus, at each well location, a direct comparison of the original geologic model and the basic engineering data was available. Fig. 4 compares total permeability thickness calculated from engineering data with that from transforms. Fig. 5 shows total or interval permeability thickness in the simulation model vs. the corresponding value from the engineering data.