multidisciplinary and integrated methodology for deepwater thin bed reservoirs characterization

8/13/2019 Multidisciplinary and Integrated Methodology for Deepwater Thin Bed Reservoirs Characterization

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Multidisciplinary and Integrated Methodology for Deep Water ThinlyBedded Reservoirs CharacterizationAchmad A.Nurhono, Budi P. Kantaatmadja, Rahim Masoudi, Goh Sing Thu, M Nasir B A Rahman, and MohamadB Othman, (Petronas) , Nina M. Hernandez, M. Ramziemran A. Rahman (Schlumberger)

Copyright 2012, Society of Petroleum Engineers

This paper was prepared for presentation at the SPE Annual Technical Conference and Exhibition held in San Antonio, Texas, USA, 8-10 October 2012.

This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not beenreviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, itsofficers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohi bited. Permission toreproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.

AbstractThe 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.

Introduction

The main objective of the study was to develop a comprehensive workflow for identifying laminated reservoirs to the finest

dynamic behavior so the major investment decisions can be made about the fieldsperformance. The setting is built-up with

limited number of exploration wells logged with new technology tools i.e. CMR (magnetic resonance), OBMI (oil based mud

image). The workflow emphasizes the methodology used to build a 3D geological model for the 10 x 10 km study area with

this constraint. The resulting model can subsequently be compared against the full range of available data in the main field,

including all development wells, production data and previous 3D static and dynamic models, in order to validate the

established assessment of a few faults in terms of their sealing capability through industry recognized techniques given the

limited data availability.


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The area of interest for this project covers the exploration and development field, which was the first deepwater oil

production in Malaysia. The deepwater basins offshore Sabah, Malaysia, are major targets for hydrocarbon exploration in this

region and are considered to contribute unique geological processes in deepwater settings commonly sandstones that are

difficult to characterize using standard techniques.

The thin laminated pay-zones are often left unexploited and perhaps even bypassed during drilling and completion. They

contain significant HC pay, finer than the conventional logging resolution, and gathering information is typically sparse. For

the first time using 7 exploration wells within three DW fields, in Sabah, Malaysia, various sets of data from coarse seismicdata to fine log resolution (OBMI and sharpened standard logs) were geologically and petrophysically interpreted to generate

reservoir model, covering three structurally separated blocks within offshore Sabah basin.

Regional Setting and Structural ConfigurationThe 7 wells within the 3 fields are located on the NW offshore Sabah basin as shown in Figure 1. Structural configuration as

shown in the seismic line (Figure 2), it is a structural complex basin that formed at the southern margin of a foreland basin

and resulted from the collision between NW and Western Sabah platforms during early Middle Miocene.

The field location is situated on a toe thrust belt where structures most likely formed after reservoir unitsdeposition. In the

Late Miocene, there was an uplift related to a regional unconformity called SRU (Shallow Regional Unconformity) and the

consequent relative sea level fall. This event shifted the facies basinward and triggered a regressive depositional episode.

Stratigraphy of Thinly-Bedded depositional ModelThe conceptual depositional model of deep water clastics has been invoked to understand the dynamics deposition of the

cyclical genetic packages, starting at the base with the MTD (Mass Transport Deposit) indicating the commencement of

sediment transport to deep water. This MTD was overlain by the thickly-bedded sands representing the frontal splays in an

unconfined non-channelized setting. And, this thickly-bedded sand was overlain by the thinly bedded heterolithics of the

channel-levee-overbank complex overlain by a depositional hiatus or occasional debris flow represented by MTD (Figure 3).

Typical channelized settings are not seen in the study area.

The whole intervals of thinly-bedded reservoirs was subdivided and labeled as interval-A to -G sands (7 thinly-bedded sand

intervals), from top to bottom, respectively. And, the thickly-bedded sand is labelled as interval-H sand (Figure 4).

Correlation of markers amongs wells required some modifications especially in the thinly-bedded (heterolithic) sand

package, as the markers were previously defined based on AI (Acoustic Impedance) only. The modifications were identified

when open-hole log responses were compared with the inherited marker breakdown.

Multidisciplines and Integrated WorkflowAs stated previously, the aim of the study was to develop an integrated multidisciplinary workflow for identifying and

characterizing thinly laminated reservoirs to the finest levels where models can be built to predict the volumetric and

dynamic behavior. The workflow emphasizes the methodology used to build a 3D geological model for the 10x10 km study

area. The resulting model was compared against the available data including development well-data, production data, also

previous static and dynamic models, in order to validate the established workflow. The nine phases of integrated multi-

disciplinary workflow for DW thinly-bedded reservoir characterization can be seen in Figure 5.

Pre-Phase1: Data Availability

The aim of pre-phase1 was to to review available data in the discovery fields. Criterion for the selection was based on: (1)

Data availability: (a) seismic data i.e. stacked seismic, pre-stack time migrated gathers, stacking velocities, interpreted

horizons, processing reports; (b) well data i.e. OBMI/FMI, triple-combo logs, MDT, formation tops, deviation survey,CMR/MR scanner, RT scanner, core analysis, and well tests. (2) Importance of fields.

Phase 1: Seismic Data Conditioning

Data Processing was done at the beginning stage, as data preparation. The objectives were: (1) to clean-up residual multiples

(MAZAP); (2) to run velocity analysis using SCVA (Spatially Continuous Velocity Analysis) techniques; (3) to apply

REVEAL (Residual Event Alignment Using Non-Rigid Matching) to provide enhanced gather flattening because accurate

reservoir property prediction or structural interpretation was highly dependent on the flatness of events on seismic gathers.

The biggest challenge of phase-1 is on the multiple/noise issues caused from strong and complex water bottom and surface

multiples, poor offset distribution, and anisotropy matters. Furthermore, since the available seismic data was only Post

Migration Stage, thus, it was limited on removing multiple/noise contents which ideally should have been done in the Pre-

Migration Stage. Western Geophysic (WG) used MAZAP method to remove the residual multiple in the dataset and followed


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by SCVA techniques to give a better stacking response. And lastly, REVEAL method was used to provide enhanced gather

flattening for a more accurate reservoir property or structural interpretation, as these interpretations were highly dependent on

the flatness of seismic gathers events. So, the main purpose of this phase-1 is to clean-up residual multiple, flattening the

gathers and prepare the dataset for RSS further work. WG managed to improve multiple attenuations and established a better

structural definition in the target zones.

Phase 2: Petrophysical SHARP Analysis

Standard resolution petrophysical analysis is often under-estimates hydrocarbon in place, especially for thinly-beddedreservoirs because these beds are thinner than the vertical resolution of most log measurements [1]. In this study, a thin-bed

evaluation technique, called SHARP, was used to estimate rock properties within the 7 wells and accurately determine the

hydrocarbon volume. The high resolution log output was then used by rock-physicists and QI-geophysicists for Neural

Network facies analysis (Phase-8), classification and fluid types (Phase-3), as well as for the Stochastic Seismic Inversion

(Phase-5).

The thin-bed resistivity analysis was used to develop a thin-bed model that yields high resolution logs from standard

resolution logs. These high resolution logs were then used in the ELAN petrophysical model to estimate a more accurate rock

properties i.e. porosity, water saturation, and permeability (Figure 6). The SHARP evaluation outputs will be used by rock-

physicist for lithology and fluid type determination, which afterward used by geophysicist in its stochastic seismic inversion

analysis.

The biggest unknown in this study is the water saturation, which seems to be quite variable in the thinly-bedded intervals.

Using bound fluid porosity estimation from CMR logs, the water saturation can still be determined. Although, it is quite

difficult to predict the lateral continuity of thinly-bedded sands, the similarity packages in the thinly-bedded sands were

demonstrated from well to well. And, this similarity was normally seen especially when the thinly-bedded sands were

associated with thick blocky sands showing the overlying thinly-bedded beds were likely to have similar lateral continuity as

the thick sands.

The SHARP analysis, which was performed in the 7 wells, resulted in a huge improvement of net-pay over the thinly-bedded

section; and as expected, there was no obvious net-pay improvement over thick sand section. These significant improvements

can be shown as follows: (1) increase of up to 18m in net-pay thickness; (2) reduction of up to 55% in clay volume; (3)

decrease of 30% in water saturation; (4) improvement of up to 4 % in porosity; and (5) improvement of up to 1000mD in

permeability [2, 3, and 4].

Phase 3: Simultaneous AVO Inversion and Rock Model Building

Lithology and fluid prediction away from well control requires understanding of how the rocksbulk and seismic propertiesare correlated to each other and also how they vary with geological environments, age, and depth. Simulataneous AVO

inversion process converts seismic dataset into a number of reservoir rock properties to characterize the thinly-laminated

reservoir package within the deep water turbiditic complexes.

Key steps in the AVO inversion processes are seismic and well log pre-conditioning, well calibrations, wavelet estimation,

low frequency model building, and the inversion itself. Initially, the angle stacks are analyzed: necessary pre-conditioning

step like time alignment is performed to improve the correlation between the angle stacks. Once the logs and seismic data are

fit to undergo inversion process, the most relevant logs such as bulk density (RHOB), compressional sonic (Vp) and shear

sonic (Vs) are calibrated to seismic. Then a series of wavelets is extracted, where the wavelet suite that exhibits the most

consistent phase on all angle stacks and maximum correlation between seismic and synthetic is chosen for the inversion. Low

frequency model, that provides the background trend for seismic inversion, is generated by extrapolating the calibrated well

logs using a number of interpreted horizons and faults together with interval velocity as a spatial guide and then lo-pass

filtered. Finally PSTM reprocessed angle stacks (Near, Mid and Far) of 3D seismic surveys of this Sabah DW were invertedfor absolute acoustic impedance (AI), Vp/Vs and Density (Rho). Other attributes such as shear impedance (SI), poissons

ratio (PR), Lambda Rho and Mu Rho were also generated using ISIS Global Simultaneous AVO Seismic Inversion package.

Figure 7 shows a workflow for the ISIS Simultaneous AVO seismic Inversion.

The rock model was also developed with the objectives: (a) to evaluate the quality of well log data of 7 wells, which are

within the seismic inversion of area of interest; (b) to determine the best elastic parameters that can be used in identifying the

lithology of the thinly-bedded reservoirs and its fluid content (gas/oil); (c) to produce an optimum porosity-acoustic

impedance transform which will be used to calibrate the stochastic seismic inversion and static reservoir modeling at well

locations and for ranking the realizations.

Elastic parameters, analyzed through various elastic parameters for lithology and fluid prediction, are generated from the

standard logs i.e. density, compressional, and shear logs. Different curves can be paired in a crossplot to determine the best


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arameters to be used for lithology and fluid prediction. Various crossplots, as a function of depth below ocean bottom

(DBOB), were constructed to evaluate variation in rock properties For lithology prediction, the crossplotting investigation

consists of: AI, VP, VS, RHOB, VP/VS, and Poissons Ratio (PR) as a function of lithology.The evaluation of fluid effects is

performed through constructed crossplots of: AI vs PR, VP vs VS, VP vs RHOB, and LambdaRho vs MuRho. The best

elastic property pairs for lithology prediction are determined to be Acoustic Impedance vs Poissons Ratio and Acoustic

Impedance vs VP/VS Ratio (Figure 8). The best elastic property pairs for fluid prediction are determined to be Acoustic

Impedance vs Shear Impedance and MuRho vs Lambda MuRho.

Phase 4: Lithology and Fluid PredictionPhase-3 has revealed that the best parameters for lithology prediction were using elastic properties relationship of acoustic

impedance (AI) and Vp/Vs ratio, while for fluid prediction, the elastic properties relationships of acoustic impedance (AI)

with Shear impedance (SI) and Lambda Rho with MuRho, were found to be good. In this phase-4, through test were carried

out to determine the final parameters to be used for lithology prediction. The agreed transforms were applied onto the

simulataneous AVO inversion results, producing probability cubes for Sand-Shale-Heteroliths (SSH, reperesent the thinly

laminated sands), thick sands, and shales. As for fluid prediction, as the focus was mainly to understand fluid distribution

within the thinly laminated sands, the fluid probability prediction was only carried out within the thinly-laminated sands

(SSH). For the SSH intervals, the fluid probability prediction produced oil, gas, and water probability cubes. These lithology

and fluid probability cubes are also used as an input in the Phase-5 which is a part of the geological/geo-statistical modeling.

The rock physics cross-plot indicates separation between litho-facies (and fluids) that is suitable for lithology prediction. The

generic flow of lithology prediction to generate litho cubes, which is adapted from Bachrach et.al workflow [5], is illustrated

in figure 9. In the prediction process, the extensive use of probability density functions (PDF) is required. Essentially

Bayesian estimation is also employed in the lithology and fluid prediction.

For this study, the inverted elastic property volumes were classified into probability volumes of thick sands, shale, and

heterolithic (thinly-bedded sands, from here on named SSH), using a Probability Density Function (PDF) prediction

approach. These litho-facies are compared with lithology prediction from the petrophysical evaluation and show consistent.

The PDFs are calculated using elastic properties from well data points over the objective intervals.

For fluid prediction, the focus of the study was on the SSH fluids e.g. the variation of fluids incorporated in the thinly-bedded

intervals. Hence, the fluid prediction was performed for SSH-oil, water, and gas. The depth trends investigation of the elastic

properties within this study indicated essentially four (4) cycles of the thinly-bedded sands. Hence the litho-facies

classification has been divided into four cycles which has been run separately with different PDFs.

Phase 5: Geostatistical Modeling and Stochastic Inversion

A 3D geological (static) model of the study area was constructed using Petrel version 2009. The main inputs for static modelwere geophysical, geological, petrophysical and reservouir engineering data resulting from previous phases of the study. Data

from 7 exploration and early appraisal wells were included in the model (wells no: 1, 2, 3, 4, 5, 6, and 7). The final model

was used to calculate a range of initial hydrocarbon volumes in places and to develop dynamic models. The following task

was performed during the modeling: structural modeling, layering and zoning, facies modeling, stochastic sesimic inversion,

petrophysical modeling, volumetric calculations, and unceratinty analysis.

5.1. Structural Modeling

The structural framework for the study area was built using seismic mapped surfaces (corrected to the well tops) and seismic

interpreted faults. Faults were extracted using ant-tracking approach in petrel; and, well tops were interpreted from well logs.

The purpose of the structural modeling is to provide a framework for stochastic seismic inversion (SSI), static, and dynamic

models. The structural (fauts and horizons) modeling of the study area covered an area of 10 X 10 kms and was break down

into 3 blocks that were separated by major thrust faults. The geological model was constructed to honour, as much as

possible, the fault geometries and structural surfaces that allowed precise hydrocarbon volume calculation.

For a depth-conversion structural grid model, a conventional approach of using a common velocity model for the entire study

area was applied. Due to involving thrust faults that have significant displacement and overlapped horizons across the faults,

it was impossible to convert a 3D-depth grid using single steps with a good result. Thus, a separate velocity model was

developed for each area within the main fault blocks; and, the 3D model using SSI method (in time) could only be converted

to depth by dividing the model within fault segments and performing the depth conversion separately for each area. The

result of this process was a 3 separate 3D model grid, in depth domain.

In order to have a single model covering all 3 main fault blocks in the geo-cellular model, the final 3D grid which will be

used for the static model had to be re-constructed in depth domain. This was done by converting the fault and surface depths

from seismic interpretation, separately. The 3D grid construction processes could then be re-run with similar parameters,

setting as the initial time model, with the fault and surface depths as the main input.


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5.2. Facies Modeling

The aim of the facies modeling was to populate litho-facies, interpreted from logs at the wells, in 3D model by honouring the

well data input, variograms, lithology prediction cubes, and conceptual model. The general steps involved in the facies

modeling procedure can be outlined as follows: upscale facies logs to geocells; variogram analysis; preparation of probability

trends; assign container facies (sand-shale) deterministically in areas with high confidence lithology prediction; stochastic

distribution of sand-shale container facies; stochastic population of litho-facies conditioned to the sand-shale model.

The lithology prediction cubes represent the probability of thick sand, heterolithic sand (SSH), and shale packages at the

resolution of elastic properties of AVO seismic inversion (4ms). Individual occurances of thick sand, SRH, and SPH as

defined at the log scale are way below the seismic resolution. Heterolithic intervals, which consist of thinly-bedded sands

(SPH and SRH), usually have intercalated layers of clean sand and shale streaks at/or above the grid cell resolution. These

small scale (< 1m) variations, which were seen in the well-logs, could not be resolved by seismic properties. So, in the 3D

model, these small scale variations of litho-facies had to be defined based on well-logs, and not based on seismic trends.

Litho-facies generated from the 7 exploration wells were defined based on core data, borehole images, and well logs. Lihto-

facies were distributed in the 3D model in order to populate the petrophysical properties. The reservoir litho-facies in the

model are defined as thick sand, sand rich heterolith (SRH), sand poor heterolith (SPH), MTD, and shale.

In addition to well logs, the lithology prediction was also based on inverted elastic properties that generated from

Determinisitic AVO inversion. This lithology prediction was also used as input for the litho-facies model. By cross plotting

AI-Vp/Vs from well data, a relationship between elastic properties and lithology could be established. The PDF (probability

density function) of each lithology, which was defined for thick sand, heterolithic sand (SSH), and shale at the seismic scale,

was applied to the AVO inversion volumes to generate lithology probability cubes (Figure 10). These were used to predict

facies distribution in the 3D model away from the wellbores.

In order to honour the large scale trends of sand versus shale that can be observed from the lithology prediction cubes, the

litho-facies were modeled in two steps. The first step, the litho-facies was merged into reservoir facies: sands (thick sand,

SRH, and SPH combined) and shales (shale and MTD combined) (Figure 11). Sequential indicator simulation (SIS) was used

for stochastic process on sand-shale population in the model. The algorithm generates irregular litho-facies shapes and does

not take into account the litho-facies sequence.

To represent the heterogeneity of the reservoir on a smaller scale, a second step was introduced to populate the final litho-

facies within the sand-shale model. The litho-facies of sand-shale distribution was modeled by populating thick sand, SRH,

and SPH within the sand containers and shale/MTD within the shalecontainers. The 2D maps , which were generated fromsurface attributes of lithology probabilities, were used as a 2D constraint in the modeling process, together with the vertical

proportion curves generated in the data analysis. The truncated Gaussian simulation (TGS) method was used to populate the

model. This algorithm models the individual litho-facies in a specific sequence and is generally used in a system where there

is a natural transition of the litho-facies. In this case, there is a transition from the thick sand (Ts), through sand rich heterolith

(SRH), to the sand poor heterolith (SPH) litho-facies.

5.3. Stochastic Seismic Inversion

A conventional 3D seismic inversion is usually used seismic data which have a relatively coarse vertical resolution, known as

Deterministic 3D seismic inversion. This coarse vertical resolution of such reservoir properties would only be effective to

resolve geologic features down to 10 m (10 ms) sand thickness under the best circumstances. Imaging and characterizing

much thinner bed reservoirs calls for a more sophisticated approach; whereby, the well control within the study area plays a

key point for modeling the reservoir layers. This process is called as stochastic seismic inversion (SSI) and plug-in within

Petrel software together with using the Ocean development framework application. This method is used to invert post stackseismic amplitude cubes for acoustic impedance in a high resolution (1 ms) geological framework. In this method, the

seismic data is inverted directly into a Petrel geo-cellular grid, where the results are immediately available at the appropriate

scale and can be integrated into the seismic-to-simulation workflows. The stochastic inversion approach generates equi-

probable multiple realizations that match well log data input. A prior model is used to constrain the vertical and lateral trends

of acoustic impedance and a variogram model is used to constrain the pattern of spatial variability of the inverted acoustic

impedance.

Stochastic seismic inversion (SSI) requires a structural framework that is built in the time domain. After completing the

geocellular model, the acoustic impedances from well logs, which have been corrected from light hydrocarbon effect and

have been fluid substituted to 100% brine for all 7 wells, were upscale to the grid at the well locations. In addition to

upscaling the well logs, the acoustic impedance from Deterministric seismic inversion was resampled into the geocellular


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grid. This acoustic impedance at seismic scale acted as a prior model of stochastic seismic inversion and also be used in the

variogram modeling (Figure 12).

The variogram model, which used in the high resolution stochastic inversion algorithm, captures the spatial variability of

acoustic impedance. The variogram model plays a key role in controlling the texture of high resolution in the inverted

realizations. Experimental variograms are calculated from the upscale AI of well data in order to characterize the AI vertical

variation. In practice, it is impossible to calculate reliable variograms in the horizontal direction, due to the sparse control of

well nature. For this study, horizontal variograms were calculated using a closely sampled AI from Deterministic 3Dseismic inversion. Spatial correlation model, which were fitted to the experimental variogram, were used as constraints in the

stochastic process. The vertical variogram was defined from 10 to 12 meter; and, the horizontal variogram was defined from

3,800 to 4,200 meter.

Prior to running a stochastic seismic inversion, the post stack full migration seismic cube was loaded into Petrel together with

seismic wavelet that was extracted during earlier phase of the study. Stochastic seismic inversion uses a combination of

upscale AI well data, seimic, variograms, AI from deterministic inversion as a prior model and runs on the 3D geocellular

grid. The workflow was performed to compute 30 realizations with high-resolution AI ouput (Figure 13). The results are

compared between the deterministic and SSI methods and are shown in Figure 14. The higher vertical resolution of well logs

shows to be propagated properly through the entire geocellular grid in the stochastic seismic inversion process, whereas the

AI shows to be blocky and blurry characters due to averaging sample of seismic data in the Determinstic 3D seismic

inversion.

The 30 realization ouput of high resolution AI from stochastic inversion were assessed in order to choose which realization to

be carried out further as a secondary variable of total porosity in the SGS model. For this purpose, the relationship between

PHIT and AI of well logs was established by cross-plot them and develop its transform relationship. The transforms of PHIT-

AI were further developed for sand (defined cut-off of volume of clay 0.45) and for shale (defined cut-off of volume of clay

> 0.45). This exercise was performed for each individual and multiple stratigraphic zones, for fault block segment, and for

whole area. The result of PHIT-AI transform training showed the user defined PHIT-AI transform could produced optimum

porosity values comparing to the PHIT-AI transforms of sand only and/or shale-sand. The later transforms either over-

estimates or under-sestimates porosity. Thus, the user defined PHIT-AI transform was chosen to be used for converting the

high resolution AI realization into total porosity in the 3D property volume model (Figure 15). The transformation was done

for each stratigraphic zone and for each fault segment.

The total porosity estimation, of 3D property volumes model coming from 30 realizations, was not the final total porosity that

would be used in the volumetric calculation. The purpose of this porosity estimation was to rank the high resolution AI

realization that would be used into depth modeling. Afterward, pore volumes were derived by multiplying the porosity andcell volume in each realization. The total pore volume of each high resolution AI realization was then estimated in term of

minimum-case (P90), medium case (P50) and maximum-case (P10). The 3 chosen realizations of high resolution AI were

then converted from time to depth domain and resampled to the final depth model from time grid to depth grid cell.

Alternatives porosity scenarios were modeled to assess the impact of porosity distribution in the model. For each litho-facies

realization, the 3 porosity realizations were generated with different AI input (P10, P50, and P90 from SSI). This was done

for 5 realizations of base case litho-facies and 5 realizations of the alternatives facies model combined with an additional

transformation within data analysis process, which was related to the compaction trend.

5.4. Permeability and Water Saturation

For obtaining relationships between porosity, permeability and water saturation (Sw) in the model, cross plots were made

using core data for different rock properties. The litho-facies of SRH and SPH represents different rock fabrics and thinly-

laminated layers of sand-silt-clay, which show in different characters on the SHARP analysis; therefore, litho-facies creates asignificant challenge to establish uniform relationships for defining rock properties across the field.

During SHARP formation evaluation, litho-facies was defined as sand, silt, and clay. At this higher resolution scale, it was

possible to recognize a good relationship for each rock type. Thus, the relationship transforms were built at the SHARP level

to obtain a robust relationship between porosity, permeability, and Sw for predicting rock/reservoir properties. The resolution

scale of SHARP level is much lower than that of the geo-cells. For permeability estimation, the transform was derived from

core data. For Sw estimation, the transform was established from log data. There was no model generated for HC transition

zones.

The permeability and Sw at geo-cell level were estimated by averaging permeabilities and Sw, which were estimated on the

SHARP analysis for each rock type and weighted by sand, silt, and clay fraction at each geo-cell in the 3D model.


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Permeability was estimated using a transform equation of: K = 1015.3351.4872

* 1.05

And, Sw was estimated using a transform equation of (as function porosity):

Swt = 21.62 * e-20.19

is the total porosity (PHIT).

For estimating permeability and Sw of each litho-facies i.e. SRH, SPH, a 3 scenario approach was conducted. Subsequently,

in the model, instead of populating litho-facies fraction and its porosity by kriging, a SGS approach was used. The 3

horizontal variograms were modeled with ranging from 500 to 5,000 designed for investigating the effect of lateral variability

of rock properties. The lateral continuity of heterolithic sand package could impact on fluid flow; thus, by varying

variograms, it was aiming to model of variable thinly-bedded sand lateral continuity. Furthermore, for estimating Sw

sensitivity, the HC in place volumes were calculated using all of alternative realizations.

Phase 6: Validation of the Geomodel

The objective of phase-6 was to validate a geological model result that was built in phase-5 and to test the thinly-bedded sand

characterization methods. This test was done by comparing the estimated permeability-Sw from geomodel against

permability-Sw from the actual dynamic data. The test was not a history matching validation and/or evaluation. Therefore,

there was no adjustment had been done to the model. And, the only adjustment done was on wellbore skins in order to match

the flowing bottom pressures (BHP). This adjustment was considered as a minor one.

After validation training in phase-6, it shows some important results, which are as follows:

(1) Phase-5 model predicts flow profile with reasonable accuracy, as shown by PLT (production logging tool) flowprofile that matches perfectly with high permeability estimation from geo-model (Figure 16).

(2) Phase-5 model predicts the sand amount within the thinly-bedded intervals was matched accurately in the recentdrilled wells and most of development wells within the field.

(3) Phase 5 model predicts accurately the historical production behavior of most of development wells in ECLIPSE.(4) Phase 5 model estimates the HC in-place volumes using Petrel and/or ECLIPSE and matches perfectly in the thick

sand but is significantly different in the thinly-bedded intervals.

Thus, it is concluded that the approach adopted in Phase-5: (1) succesfully characterized the thinly-bedded intervals at well

bores where higher resolution logs were presence; (2) using seismic inversion guidance and a proper geological model, the

model could distribute the thinly-bedded sands with reasonable accuracy across the field; (3) could predicts HC and flowpotentials in the unexplored region in the field.

6.1. Analysis of PLT Data in Thin Bed Zones

The PLT log was run across 2 thinly-bedded intervals in a well within the study area. The conventional log interpretation

indicates that there is almost no permeability in the thinly-bedded intervals. Therefore, any simulation model that uses such a

permeability would predict almost no flow from these thinly-bedded intervals. However, the PLT log indicates that

approximately 70% of flow comes from the thinly-bedded intervals in this well. Furthermore, after reviewing this study, the

new model shows a substantial permeability presence in the thinly-bedded intervals; and, with this magnitude of

permeability, it would be expected to contribute fluid flow of at least 50% from the total production (Figure 16). The

permeability comparison between the new model and the previous model can also be seen in figure 16. And, using the new

estimated permeability, the production log model can be forecasted using ECLIPSE simulation.

Phase 7: Uncertainties AnalysisThere are two main objectives in phase-7, which are as follows:

(1) Explore the actual uncertainty in phase-5 model, which specifically are:

a) Examine variability effects of using different rock properties in different realizations that were used in phase-5, onpredicting WBP (well block pressures) in the study area.

b) Determine which (if any) of these rock property realizations showing unsatisfactorily prediction on the WBP inthe study area.

(2) Investigate key factors that are not considered by variability effects of different rock property realizations that were

conducted in phase-5 model. These include:

a) The factors that can significantly disrupt the ability of phase-5 model to accurately predict the observedproduction history in the study area. Such factors are not acceptable and cannot be used in thinly-bedded model in

this study.

b) The factors that significantly affect oil and water productions from base case in phase-5 model.


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In this uncertainty analysis, the summary was as follows:

1. The new built model was highly controlled; thus, it did not show much variation.2. Uncertainty variability in rock properties and sand lateral continuity of thinly-bedded intervals i.e. SRH and SPH

significantly affect the prediction.

3. A more exact control is required to make a better and perfect model.

Phase 8: Sedimentological AnalysisThis phase analysed sedimentary structures, rock fabrics from cores combined with dip estimation from OBMI logs. There

are 4 litho-facies classified in this study, which are as follows: thickly-bedded sandstone (Tsh), thinly-laminated heterolithic

intervals (Ssh), mass transport deposits (MTD), and Shale. Furthermore, the thinly-laminated intervals can be subdivided into

sand rich (high N/G) and sand poor (low N/G) packages.

The input curves that used for neural network litho-facies determination were: volume of clay (Vcl), acoustic impedance

(AI), ratio of compressional and shear wave velocity (Vp/Vs), ratio of coarse and fine-grains from CMR logs, and higher

resolution density that derived from OBMI.

The gravity flows sediment was interpreted mainly turbidite sediments representing by thick, thinly-bedded, and MTD (i.e.

debris flows, slumps, and/or slides) deposits. Furthermore, the thinly-bedded sediments are deposited mostly in the levee-

overbank complexes, which complexes are abundant with thinly-bedded sands varying from 2 to 20 cm in thickness, as can

be seen in cores and image-logs. The OBMI image log usually is capable to estimate the thinner sands of 2 - 3 cm.

Phase 9: Geomechanical Rock Property Analysis

Mechanical and petrophysical propertiy tests were conducted quite thoroughly on shales and sandstones cores from the cored

well in field.

The test program consisted of:

(1) single-stage triaxial compression tests and verifed with ultrasonic velocity measurements on shale samples at room

temperature, for Mohr-Coulomb failure envelope delineation; (2) multi-stage triaxial compression tests and verified with

ultrasonic velocity measurements on sandstone samples at room temperature, for Mohr-Coulomb failure envelope

delineation; (3) pore volume compressibility tests using conventional effective stress loading under uni-axial strain boundary

conditions to simulate production and verified with horizontal permeability measurements; (4) pore volume compressibility

tests with full pore pressure depletion under uniaxial strain boundary conditions to simulate production; and (5) basic

petrophysical properties i.e. pulse decay shale permeability; and porosity and permeability that were required for pore volume

compressibility tests for sandstone.

The purpose of the testing was as follows:

(1) provide rock-strength information for developing a rock failure envelope. With sufficient rock-strength measurements on

core samples together with other information such porosity, the rock- strength estimation may be possible using well-log.

Furthermore, with these test results, the borehole stability and/or sand production predictions can be performed. (2) provide

static and dynamic geo-mechanical properties information for correlating well-logs. Logging-based measurements are in the

kilo-hertz range; whereas actual physical loading rates acting on a wellbore are generally much lower, known as pseudo-

static. Furthermore, the hydraulic fracturing is also a pseudo-static method. This is the technical justification for conducting

laboratory pseudo-static tests for measuring Youngs modulus (E) and Poissons ratio (). And, at the same time, it measures

dynamic responses of core samples, in term of high and low loading rate. This dynamic response is needed for well-log

calibration in order to provide realistic deformation parameters (E, ) for engineering purposes. (3) Provide parameters for

completions and stimulation design. (4) Provide parameters for reserves estimation, production forecasting, reservoir

compaction, permeability change, and subsidence predictions using comprehensive pore volume compressibility tests.

Conclusion

This study was considered to be successful in developing a unique deep water workflow and showing methodologies for

modeling thinly-bedded reservoirs, especially having a satisfactory validation result comparing the new model and available

production data. In the study area, there are only 7 exploration wells that can be done on SHARP and cascading seismic

inversion (AVO simultaneous inversion and stochastic inversion) analysis that were used as input data for the subsurface

model. As a whole, higher reserves of about additional 30% were estimated in the thinly-bedded intervals, and the accuracy

of the model was validated with the existing dynamic data.


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Acknowledgments

We would like to thank to Petronas management for allowing us on using the data for investigating and funding this study of

the thinly-bedded reservoir potential in deep water areas in Sabah, Malaysia.

References

1. R. Bastia, A. Tyagi, K. Saxena, T. Klimentos, R. Altman, S. Alderman, S.Bahuguna, Evaluation of Low-Resistivity-PayDeepwater Turbidites Using Constrained Thin-Bed Petrophysical Analysis, SPE 110752, SPE Annual Technical

Conference and Exhibition, , Anaheim, California, U.S.A, 11-14 November 2007.

2. B. P. Kantaatmadja, A. M. Hassan, M.N. Hisham M. Azam, M. A. Rasheed, R. Leech, Successful Application of ThinBed Petrophysical Evaluation Workflow in Deep-water Turbidite Environment: Case Studies from Fields Offshore

Malaysia, Petroleum Geology Conference & Exhibition (PGCE), Kuala Lumpur, Malaysia 201 0.

3. M. Claverie, H. Azam, R. Leech, G. V. Dort: A Comparison of Laminated Sand Analysis Methods ResistivityAnisotropy and Enhanced Log Resolution from Borehole Image, Petroleum Geology Conference & Exhibition (PGCE),

November 2006, Kuala Lumpur.

4. S. Daungkaew, M. Claverie, B. Cheong, S. Hansen, R. Leech, M.N. Hisham M. Azam, E. Malim, M. R. Lasman, R.

Witjaksana, Forecasting the Productivity of Thinly Laminated Sands with a Single Well Predictive Model, SPE AsiaPacific Oil & Gas Conference and Exhibition (APOGCE), Perth, Australia, 2008.

5. R. Bachrach, M. Beller, C. C. Liu, J. Perdomo, D. Shelander, N. Duta Combining Rock Physics Analysis, FullWaveform Prestack Inversion and High Resolution Seismic Interpretation to Map Units in Deep Water A Gulf of Mexico

Case Study, The Leading Edge, 2004.

Figure 1: Location Map of the study area


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Figure 2: Structural Geology of the Study area, A Toe Trust Belt.

Figure 3: Conceptual Model of DW Thinly Bedded Reservoir Geology

Figure 4: Stratigraphy Setting of MTD Fan Complex Description


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Figure 5:Multi-disciplinary workflow for DW thinly-bedded reservoir characterization

Figure 6: Result comparison of standard and high resolution log evaluations inone of the thinly-bedded intervals in XY well, verified with cores (red and green dots).


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Figure 7: ISIS Simulataneous AVO Inversion workflow

Figure 8: Elastic Properties Relationship 1ms sampling for AI vs PR and AI vs VP/Vs


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Figure 9: Diagram showing how Bayess Rule work for lithology estimation.

Figure 10: Facies Modeling 1st step in building the Sand-Shale Model


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Figure 11: Facies Modeling 2nd step in building the Lithodepo Facies

Figure 12: Cascading inversion showing an input from AVO inversion resultsgo to Stchastic Seismic Inversion.


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Figure 13: Stochastic Seismic Inversion input and 3 chossen output of High Resolution P10, P50 and P90 AI in Time domain.

Figure 14: comparison results between Deterministic Simultaneous AVO Inversionwith Stochastic Seismic Inversion (SSI).


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Figure 15: AI-Porosity Transform for generating porosity maps

Figure 16: PLT Test comparison in XY well between Old and New Models

multidisciplinary and integrated methodology for deepwater thin bed reservoirs characterization

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