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New Life for Old Data: Brownfield Reservoir Characterization and 3D Previous HitGeologicalNext Hit Modeling from the West Baram Delta Province, Offshore Sarawak, Malaysia*

By

Matthew Mulcahy1, Tanwi Basu1, A. Wahid Musbah2, Gavin Douglas1, Howard D. Johnson2, Kamarolzaman B. Yahya2, and Mustafa Suleiman2

 

Search and Discovery Article #20023 (2004)

 

*Adapted from presentation at the AAPG Annual Convention, Dallas, Texas, April 18-21, 2004. 

1Schlumberger DCS, Kuala Lumpur, Malaysia ([email protected])

2Petronas Carigali Sdn Bhd, Kuala Lumpur, Malaysia ([email protected])

 

Abstract 

Reservoir characterization studies of the multiple, vertically-stacked Miocene reservoirs in the Bokor and Betty fields (West Baram Delta, offshore Sarawak [Figure 1]) have created facies based 3D Previous HitgeologicalNext Hit models to appraise remaining reserves and determine strategies for economic redevelopment. This paper describes the reservoir modeling workflow (Figure 2) and highlights lessons learned for similar redevelopment projects in the future.

 

Since the discovery of these fields in the 1970’s, extensive subsurface data, including core, well log, dipmeter, seismic and production data have been used to evaluate these Late Miocene coastal/deltaic reservoirs. However, previously these data had only been evaluated in traditional, 2D form and had never been subject to a modern 3D Previous HitgeologicalNext Hit modeling analysis. To do this, and to supplement the existing data, further in-depth studies were performed, including (A) petrography (SEM and XRD analyses) to determine framework grain and clay mineralogy and their distribution (pore-filling vs. pore bridging as shown in Figure 3, (B) rock fabric/texture analysis from high-resolution dipmeter data to assess fine-scale Previous HitheterogeneitiesNext Hit (Figure 4) and (C) facies analysis of core and well logs to generate electrofacies models (Figure 5).

 

 

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Figures Captions

Figure 1. Location map showing location of Betty and Bokor fields, in the Baram Delta province, offshore Sarawak, Malaysia. Area of more than 4 km sedimentary section is outlined. (After Ngah, 1999).

Figure 2. Workflow recommended for brownfield geomodeling, illustrating new life for old data.

Figure 3. Petrography through SEM and spot elemental analysis of Betty sandstones showing angular pores within frame-working quartz grains (left) and pore-filling chlorites (right). XRD analysis independently performed on the shales also resulted a clay percentage dominated by chlorite and minor illite.

Figure 4. Rock Fabric Analysis via BorTex using the dipmeter logs showing least active microresistive curves for massive, poorly stratified sandstone (left) in Betty-5. Highly active curves are observed in clay-clast bearing storm-dominated-event beds (top-right) and intensely bioturbated sandstones, both of which show high conductive heterogeneity as calculated from the analysis (red shading on the heterogeneity track). This analysis captured the fine scale depositional heterogeneity as conductive and resistive anomalies.

Figure 5. Electrofacies Analysis using neural network techniques via RockCell. The diagram illustrates the raw input log data on the far left that is explicitly tagged from core description for neural network training. In the middle are the facies probability and final facies estimation, which can then be checked against original core description on the right.

Figure 6. Example of genetically-based electrofacies distribution for one reservoir unit in Bokor Field. Seismic amplitudes and reservoir engineering data, together with core results, combine in construction of the 3D Previous HitgeologicalNext Hit model.

Figure 7. Hydrocarbon pore volume thickness map (HCPVo) derived from simulation results on 3D Previous HitgeologicalNext Hit model allow identification of infill targets and recompletion opportunities

Figure 8. A cross-section panel from the SSW (left) to ENE (right) going through the depositional strike of L3 reservoirs in Betty field showing lateral continuity of most of the best reservoir quality sandstone (in yellow) and inner-neritic shales (in gray). Vertical heterogeneity is also a function of the distribution of poor quality sands (in orange). The tracks for each of the wellbores shows the volume clay and the lithofacies distribution as estimated from RockCell neural network technique in the left and the right, respectively.

Figure 9. A North (left) to South (right) cross-section panel from Bokor field, illustrating the contrast between laterally heterogeneous lower coastal plain deposits (P) and laterally continuous shoreline/shoreface (C) and shoreface/shelf (S) deposits. Vertical heterogeneity is prevalent throughout, resulting in numerous stacked reservoirs. Tracks for each wellbore show the volume of clay infilled, with electrofacies on the left and porosity and permeability on the right.

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General Statement 

Although core data were extremely limited in Betty and Bokor fields, they were recognized to be critical to the reservoir modeling objectives. Consequently, a key approach was the development of electrofacies models that integrated both the core and wireline log data. In addition, it was necessary to review the conceptual and analog depositional models in order to provide the optimum framework for understanding facies and sand body distribution. This, together with analysis of well log patterns and seismic amplitudes, enabled propagation of a genetically based, electrofacies interpretation to each of the hundreds of reservoirs within every well within the two fields (Figure 6). This was the precursor to distributing facies and rock properties data in 3D geocellular models for each field; this has significantly improved reservoir understanding and enhanced redevelopment decisions. In particular, the 3D Previous HitgeologicalTop models provided the basis for history-matched full-field simulation models, which have identified new and timely infill well targets and recompletion opportunities (Figure 7). Finally, these studies have also provided additional insight into the depositional setting and stratigraphic architecture of these multiple stacked (100s-1000s ft thick) wave- and tide-influenced coastal/deltaic reservoirs. Additional details of the latter are summarized below.

 

Betty Field 

The Upper Cycle V (Upper Miocene) reservoirs in the Betty Field reflect repeated progradation and retrogradation of the north-westward prograding West Baram Delta. Generation of accommodation space was strongly influenced by episodic movement of the bounding growth fault to the south. Facies and rock property characteristics show that the reservoirs were highly wave reworked and redistributed alongshore to form laterally continuous shoreface sand sheets with associated transitional to offshore inner-neritic shelfal lithofacies. The reservoirs are highly heterogeneous vertically (Figures 7 and 8), due to the frequent intercalation of inner-neritic shales poorer quality distal lower shoreface and transition zone lithofacies, particularly in the lower part of each coarsening upward parasequence.

 

Bokor Field 

The Upper Cycle V/Lower Cycle VI (Upper Miocene) succession in the Bokor Field displays extreme vertical stacking of approximately 130 separate reservoirs over a 6000-ft-thick hydrocarbon-bearing interval. The reservoirs (Figure 9) were deposited in a more variable coastal/deltaic environment, which includes distributary channels, mouth bars, tidal channels/estuaries, tidal flats and coastal barrier/shoreface sand bodies. The Bokor reservoirs were deposited in a more axial deltaic setting, when the coastal and inshore areas were influenced by both tidal and wave processes, similar to the modern Niger Delta. The greater abundance of channel sand bodies results in a higher degree of lateral heterogeneity, as is evident from seismic amplitude displays. The latter confirm the gross paleogeographic setting of the Bokor Field and provide independent data on channel size, shape and orientation.

 

Conclusions 

These studies demonstrate how modern techniques and technologies can integrate highly variable vintages of subsurface data into robust reservoir models capable of improved reservoir understanding and more confident prediction of bypassed oil location. They also highlight the additional value that can be gained from existing and sometimes apparently neglected or under-utilized data. Finally, we would like to emphasize how modern technologies can be used to delivery timely results in these and other brownfield reservoir characterization studies. The approach described here could be applied to many other redevelopment projects.

 

Reference 

Ngah, Khalid, 1999, Malaysia’s gas resources: Search and Discovery article #10002 (http://www.searchanddiscovery.net/documents/Malaysia/gas.htm#sarawak).

 

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