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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.
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.
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.
This paper describes how petrophysical thin-bed analysis is applied to an integrated static and dynamic modelling workflow to obtain a history match based on 3 years of production, for a series of relatively thin heterolithic reservoirs. Previous reservoir simulation work based on conventional petrophysical interpretation for property modelling, indicated insufficient connected STOIIP and permeability-thickness to match flow behaviour observed from surveillance data. Therefore, an alternative thin-bed approach was proposed to address this fundamental reservoir characterization issue. It is well known that across highly-laminated sandstone-shale intervals, the acquired log measurements of the sandstone laminations are adversely affected by shoulder effects due to inadequate vertical resolution of most logging tools. Furthermore, the resistivity of thin sandstones is suppressed by the high conductivity of silt-clay laminations which further compounds the problem. Thisleads tothe underestimation of reservoir properties and consequently, in the underestimation of hydrocarbon volumes and permeability-thickness. The thin-bed approach utilises available core and high-resolution resistivity-based wellbore images together with open-hole logs. These are used as inputs to generate a set of petrophysical properties, via a log resolution enhancement (LRE) method, which are more representative of the reservoirs under study. The petrophysical improvements made, relate particularly to net pay thickness, porosity, permeability and saturation estimations. This paper also demonstrates how thin-bed properties are propagated into the static modelling workflow, to produce a series of realizations which results in improved reservoir characterization, with more accurate in-place volumes and flow characteristics. In practice, the application of thin-bed analysis requires careful refinement to 3D grid design so that the effects of thin-bed heterogeneity are captured to facilitate history matching in simulation. By integrating this thin-bed approach, an improved history match is obtained more efficiently and without significant application of local modifiers. This improvement further infers that thin-bed log analysis is much more appropriate than ‘conventional’ log analysis for thinly-bedded heterolithic reservoirs not only in this field, but potentially to many similar reservoirs in this basin, and elsewhere. This work ultimately led toa successful infill drilling programme and opened up potential for extended development to include secondary recovery; as opposed to ad-hoc workover potential, as described in the original Field Development Plan.
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