Low resistivity low contrast (LRLC) pays/intervals mostly are being examined via behind casing analysis and produced thru production enhancement works from idle wells in numerous oil fields in Malaysian Basin. However, with the aim of unlocking hydrocarbon volumetric LRLC potential, it should include seismic data analysis in addition to formation analysis behind casing. In the past, these LRLC intervals were often ignored and considered as water-wet sands due to high water saturation or as tight sands. These intervals, which contain significant reserves, have been recognized and presented in several technical papers explaining its appropriate identification and evaluation techniques using well-data (logs and samples/cores). The economic importance of LRLC pay sands in the Malaysian Basin has just recently been demonstrated. These pays may lead to a large areal extent and may contain ten to hundred millions barrels of hydrocarbons. The integration and proper techniques of petrophysics and geophysics may become a vital approach for understanding the hydrocarbon distribution (volumetric) within a large areal extent. The possible environment of depositional for developing LRLC reservoirs are: 1) deep water fans including MTD (mass transport deposit), 2) turbidites, 3) shoreface (middle to lower part), 4) delta complex (i.e. delta front and toe deposits), and 5) channel fills. The LRLC may not occurred in alluvial fans and aeolian deposits. These 5 environment of depositions may accumulate several types of LRLC sediments such as: laminated intervals; bioturbated intervals (with dispersed and/or structural clays); altered framework grains and/or smaller grain size (i.e. silty sand reservoir). The thinly laminated sand is probably the most significant reservoir in term of producing hydrocarbon comparing to the other LRLC types. The understanding of logging tools (wireline, LWD) and its responses can be used to build petrophysical and rock-physic models that can evaluate these LRLC reservoirs. But having available advanced logs such as NMR, Image, sonic (Vp and Vs) logs, it is a plus for a better analysis. Furthermore, an elastic property (rock physics) from seismic data may establish a clear separation for different lithology and fluid saturation on LRLC sands and may lead to future recommendation work on LRLC reservoir characterization. The objective of this paper is to provide the above understanding with explanation and examples. Introduction In Malaysia basins, the LRLC (Low Resisitivity Low Contrast) sands are being recognized and brought into production recently. Previously, these sands were considered negligible in term of its commercial; but now, it has enough evidence confirming by available well-data that these sands are of economic importance. With occurrences of numerous productive intervals have been indentified, it encourages us on understanding further on its geologic background, depositional scheme, geophysic assessment, and proper formation evaluation from available well-data such as well-logs, cores, and production history.
Thinly bedded reservoir study in the deep-water area, offshore Sabah, Malaysia, was performed with the primary objective of improving the understanding of its complex geology. The nature of reservoirs, which are predominantly thin-bed and laminated sandstones of submarine fan environment, contain a high level of uncertainty in its lateral continuity. Standard shaly-sand log analysis methods contribute pessimistic values of porosity and water saturation when applied to these reservoirs. Few techniques are then presented for the determination of these rock properties, which are more reliable with core and production data. Core grain-size analysis of these reservoirs shows that clay content is generally low but the silt content can be significant. Furthermore, log responses show that porosity distribution and mineral-conductivity are influenced mainly by the silt-size particles. A sand-silt-clay (SSC) model was then developed from density-neutron crossplot, which model is also used to determine porosity and water-saturation in addition to volumes of lithology components of the reservoirs. Furthermore, other petrophysical technique, called SHARP, uses 1D convolution filters to match thin bed modelled log curves to their corresponding measured responses. A petrophysical evaluation using standard resolution logs and the thin bed resistivity (SRES) from image response are used to develop a thin bed model that yields high resolution logs. For zones where the resistivity image indicates significant thin bed development, the standard petrophysical analysis should also indicate the existence of free fluid. Although the porosity tools cannot resolve the thin beds, they nevertheless represent the bulk volumetric over the interval, known as Thomas-Stieber-Juhasz (TSJ) method, and would be able to differentiate between porous zones with lower clay volume versus porous shales with high clay volumes. The main point is that if a thin bed interval has some calculated free fluid volume using standard resolution logs, then a thin bed analysis is warranted.
The sedimentation in deepwater environments commonly includes deposition of thinly-bedded pay zones that are difficult to be characterized using standard seismic and logging techniques. Furthermore, these zones are often left unexploited and even overlooked during drilling, as they are finer in resolution than it can be detectable in conventional open-hole logs. The paper presents an integrated multi-disciplinary study on thinly-bedded reservoir characterization in deep water areas in Malaysia. The adapted workflow consist of: (1) Seismic Data Conditioning, (2) Petrophysical SHARP Analysis, (3) Simultaneous and Rock Model Building, (4) Lithology Prediction, Hydrocarbon Volume, and Net pay, (5) Stochastic Seismic Inversion and Geo-statistical Modeling, and (6) Reservoir Simulation and Validation, (7) Uncertainty Analysis, (8) Sedimentological Analysis using Core-Image, and (9) Geomechanical Rock Property Analysis. Petrophysical diagnostics using high quality resistivity images of OBMIs, as log input for thinly-bedded modeling, was the primary driver to establish effective elastic properties through AI vs. VP/VS cross plot (for lithology prediction) and AI vs. total porosity cross plot (for porosity prediction) within the model. These cross-plot transforms are then upscaled and applied to build a cascading of deterministic inversion (simultaneous AVO inversion) and stochastic inversion of 1-ms sampling, which are calibrated to core and neural network litho-facies interpretation for lithology and porosity modeling. The geo-statistical modeling workflow was initially built-in with 7 exploration wells that have OBMIs (Oil Base Micro Imager) as the typical model. Numbers of reservoir properties realizations were generated by generating geo-cellular grid over the zone of interest. These realizations could provide an improved lithology, porosity and fluid determinations and could lead to estimate a more robust volumetric, particularly within such thinly-bedded reservoir. The developed unique integrated workflow was applied on the field under study showing about 30% increase in in-place volume and was successfully validated against available production/well data as well as new drilled wells.
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