The Missing Link--Identification of Reservoir Compartmentalization Through 
Downhole Fluid Analysis

Elshahawi, Hani; Hashem, Mohamed; Mullins, Oliver C.; Fujisawa, Go

doi:10.2523/94709-ms

Cited by 21 publications

(14 citation statements)

References 10 publications

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“…Some of the new measurements such as in-situ GOR, pH [9] , density [10] , fluid composition [6] including CO2 [11] may help in differentiating small differences in fluid composition. This technique can complement the routine PVT sampling and analysis and help identification of compositional gradient [12] and compartmentalization [13] . The GOR measured in these samples closely matched with GOR that was later measured during DST.…”

Section: Fluid Scanning Using Dfamentioning

confidence: 99%

Advanced Formation Testing and PVT Sampling in Deep Gas Condensate Reservoir: Case Study from Malaysia

et al. 2009

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TX 75083-3836, U.S.A., fax +1-972-952-9435. AbstractDownhole sampling in gas condensate reservoir is well known to be challenging due to the nature of near critical fluids. Reservoir fluid properties can change dramatically with slight changes in reservoir pressure and temperature. As a result, accurate and representative PVT data are essential for reservoir fluid modeling and field development planning but difficult to obtain using conventional sampling techniques. This paper presents the first successful downhole gas condensate sampling in a high pressure gas condensate field, offshore East Malaysia. Samples collected from the previous surface tests showed large variation in Condensate Gas Ratio (CGR) from 50 to 200 stb/mmscf. This resulted in large uncertainty in the dew point pressure, condensate yield, well productivity, and reservoir fluid type. There was strong need to acquire high quality downhole samples to reduce these uncertainties, which can potentially affect the entire field development plan. Through the use of new technology and an integrated team approach, it was possible to take representative single phase fluid sample using controlled drawdown and real time fluid analysis of downhole sample.There were several key challenges in this operation. The team had to take single phase gas sample, with minimum contamination in a High pressure High temperature (HPHT) well drilled with Oil Based Mud (OBM), station time had to be as short as possible to avoid tool getting stuck, and have an initial estimate of dew point pressure and Gas Oil ratio (GOR) from downhole measurements. This was achieved using real time data monitoring and control of the entire wellsite operation from PETRONAS Carigali office. The latest downhole fluid identification tool was used along with focused sampling to minimize OBM contamination. This paper will highlight the effective use of various elements of new technology and team work.Fluid density measurement was found useful in answering some of the questions. It allowed comparison with optical fluid analyzer to provide an improved fluid identification. It also allowed to optimize the number of pretests and hence reduce the rig time and cost. By measuring the change in fluid density during clean up, the in-situ density tool also complemented other spectrometer based optical analyzers in determining the contamination level during sampling process.In this complex gas reservoir, there were potential reservoir compartments of different gas composition. Fluid samples from different zones confirmed the presence of such compartmentalization. The deeper zones showed much leaner gas composition compared to the shallower intervals. The knowledge of in-situ dew point pressure from downhole fluid analyzer was used to ensure a single phase gas sample during wireline sampling. This information was later used to design a well test to keep flowing bottom hole pressure above dew point pressure and thus obtain representative surface fluid samples. This paper demonstrates how a proper job planning, r...

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Section: Fluid Scanning Using Dfamentioning

confidence: 99%

Advanced Formation Testing and PVT Sampling in Deep Gas Condensate Reservoir: Case Study from Malaysia

et al. 2009

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“…Obviously, a reservoir model constructed while bearing depositional facies in mind is superior to any models that are built without considering depositional processes. Secondly, variation of depositional facies results in reservoir connectivity and compartmentalization, which are the key issues impacting reservoir development (Smalley and Hale, 1996;Slatt and Stone, 2001;Elshahawi et al, 2005;Snedden et al, 2007, Larson andStraub, 2007;Clayton, 2008;Chen, 2011;Akingbade et al, 2011). Failure to recognize the complexity of facies distribution may result in unrealistic estimate of the total reserve and ultimate recovery.…”

Section: Impact Of Deepwater Depositional Facies On Reservoir Modelsmentioning

confidence: 99%

Understanding Uncertainties in Deepwater Depositional Facies Modeling for Better Reservoir Conditioning

Chen

2012

SPE Europec/Eage Annual Conference

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In deepwater reservoir modeling, the proportions of various facies being modeled have a significant impact on the distribution of reservoir properties, total reserve, reservoir connectivity, and hence recoverable reserve. Generally, facies modeling can be achieved by deterministic or stochastic approaches, or a combination of both. When seismic data have sufficient quality or resolution, the data may serve as certain reservoir property indicators, and the approach for facies modeling can be more deterministic. When seismic data are not available or do not have sufficient resolution, a stochastic approach is usually needed. In either case, the uncertainties in facies distribution must be fully assessed. This paper presents a case study and workflow in which different sand-shale proportions (more optimistic, basic, and less optimistic) are considered in facies modeling, in order to investigate the range of deepwater turbidite facies distribution and its effect on reservoir property variations. The facies models are constructed by different methods, including deterministic, stochastic, and a combination of both. Comparison of the deterministic and stochastic models provides certain insight into the distribution of depositional facies. Further, in each facies scenario, the range of reservoir property distribution is assessed. In addition, for different reservoir zones, the ranges of oil-water contacts are incorporated in the reservoir model. As a result, the entire spectrum of in-place hydrocarbon volumes is captured, and various stacking patterns as well as connectivity issues can be conditioned in the simulation model, leading to a more robust understanding on the resource to be developed.

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“…Kaufman et al 1990a;Elshahawi et al 2005) or recent external processes affecting the reservoir (e.g. Montel et al 1993;Berg et al 1994;Zawisza 2004), if they diverge from a steady state distribution.…”

Section: Reservoir Fluid Mixing Processesmentioning

confidence: 99%

“…The reservoir is homogeneous with a uniform permeability of 100 mD and porosity of 0.2. GET IT BEFORE IT GETS YOU reservoir compartmentalization (Kaufman et al 1990b;Smalley & Hale 1996;Elshahawi et al 2005). Other workers have focused on methods for determining the final, vertical steady state distribution of hydrocarbon components due to the interaction between gravitational segregation and molecular diffusion (Schulte 1980;Holt et al 1983;Høier & Whitson 2001;Ratulowski et al 2003) and sometimes thermal effects (Jamet et al 1992;Pederson & Lindeloff 2003).…”

Section: Molecular Diffusionmentioning

confidence: 99%

Reservoir compartmentalization: get it before it gets you

Smalley

Muggeridge

2010

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This paper examines the impact of compartmentalization on oil recovery, the importance of identifying it during field appraisal, and methods to evaluate it using fluid data. The impact on recovery factor is highlighted using a global database of oil field recovery factors as a function of reservoir complexity and compartmentalization, and emphasized in two case studies. The effect of compartmentalization on oil recovery demonstrates the benefit in characterizing compartmentalization correctly during appraisal, so that the field can be developed in an optimal manner.Early characterization of field compartmentalization requires making maximum use of available fluid data during appraisal. When interpreting fluid data to identify compartmentalization, it is critical to take into account the different time-scales for various fluid signals (pressure, contacts, density, composition) to equilibrate, and to be able to extrapolate to field production timescales. This is essential to avoid false negatives (compartments assumed absent due to homogeneous fluid properties, when in fact fluids would have equilibrated even in the presence of compartments), false positives (where fluid differences are interpreted as evidence of compartments when in fact there has not been sufficient time for equilibration to occur), and to resolve apparently conflicting data (some fluid indicators are at equilibrium, others are not). Rigorous simulation of fluid equilibration is a complex multiphase multidimensional process, and is generally reserved for specialist in-depth studies. However, order-of-magnitude evaluations can be made using analytical solutions in minutes, allowing many 'what-if' scenarios to be considered and uncertainty to be assessed.Analytical solutions for estimating the time required for spatially-varying fluid properties to revert to steady state distributions are reviewed. All these mixing processes are shown to be diffusive in character. An effective diffusion coefficient for each process can be calculated from the reservoir rock and fluid properties. For an isothermal system, the different time-scales and distances for each fluid-property variation to attain equilibrium can be compared on a single graph. Where the time elapsed since fluid-perturbation is known, analytical solutions can be used to estimate the degree of compartmentalization (e.g. permeability of barriers). These solutions lend themselves to the development of simple practical compartment-assessment tools for industry practitioners.

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The Missing Link--Identification of Reservoir Compartmentalization Through Downhole Fluid Analysis

Cited by 21 publications

References 10 publications

Advanced Formation Testing and PVT Sampling in Deep Gas Condensate Reservoir: Case Study from Malaysia

Advanced Formation Testing and PVT Sampling in Deep Gas Condensate Reservoir: Case Study from Malaysia

Understanding Uncertainties in Deepwater Depositional Facies Modeling for Better Reservoir Conditioning

Reservoir compartmentalization: get it before it gets you

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