A method based on spontaneous imbibition for characterization of pore structure: Application in pre-SCAL sample selection and rock typing

Mirzaei‐Paiaman, Abouzar; Saboorian‐Jooybari, Hadi

doi:10.1016/j.jngse.2016.09.023

Cited by 52 publications

(14 citation statements)

References 35 publications

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“…Identifying regions with similar features (definable and statistically predictable properties) for better characterization and modeling of reservoirs is known as rock typing [2]. In comparison, special core analysis involves more fluid flow laboratory experiments and energy measurements, including: two-phase flow properties, capillary pressure, wettability, and relative permeability [3][4][5]. Generally, any meaningful classification that differentiates and describes the reservoir based on special characteristics might be attributed to rock typing [6,7].…”

Section: Introductionmentioning

confidence: 99%

“…Petrophysical rock typing has been used in many energy studies (e.g., net-pay cut-off definition) [10] and predicting the permeability of un-cored reservoirs [11][12][13]. However, the most significant energy engineering applications that can directly impact the simulation models output and their reliability are representative sample selection for SCAL tests by reducing the number of required representative samples [1,4,14,15] and determining saturation functions for static/dynamic reservoir modeling [2].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Energy Lifting Approach Using J-Function and Flow Zone Indicator for Oil Fields

Tarhuni¹,

Sulaiman²,

Jaafar³

et al. 2022

Energy Engineering

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Energy Lifting Approach Using J-Function and Flow Zone Indicator for Oil Fields

Tarhuni¹,

Sulaiman²,

Jaafar³

et al. 2022

Energy Engineering

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“…Petrophysical rock typing has a wide variety of applications such as: drilling (e.g., prediction of high mud-loss intervals), production (e.g., potential production zones, locating perforations, diversion system design in acidizing, and prediction of high injectivity zones) (Roque et al, 2017, Oliveira et al, 2016, reservoir studies (net-pay cut-off definition) (Kolodzie, 1980, Saboorian-Jooybari, 2017, representative sample selections for special core analysis (SCAL) tests (Siddiqui et al, 2006, Serag El Din et al, 2014, Mirzaei-Paiaman and Saboorian-Jooybari, 2016, permeability prediction in uncored intervals (Amaefule et al, 1993, Abbaszadeh et al, 1996, Davies and Vessell, 1996, Soto et al, 2001, Taslimi et al, 2008, Askari and Behrouz, 2011, Sokhal et al, 2016, Chen and Yao, 2017, Zhang et al 2018, Wang et al 2018, and defining saturation functions for static and dynamic reservoir models (Mirzaei-Paiaman et al, 2015, Askari andBehrouz, 2011). Generally the literature core-based petrophysical rock typing methods can be classified into three separate categories.…”

Section: Introductionmentioning

confidence: 99%

“… Methods based on capillary pressure ( ) data such as a J-function, the empirical grouping technique, parameterization Xu and Torres-Verdín, 2013;Lin et al, 2015) and measured r35 (Kolodzie, 1980).  The spontaneous imbibition rate-driven method of FZI** or FZI-Double Star (Mirzaei-Paiaman and Saboorian-Jooybari, 2016) Among the above methods, the first category has caught major interest in industry and academia since its indices do not require prior knowledge of capillary pressure and/or relative permeability data. However, similar indices have been proposed that need SCAL-driven parameters (Nooruddin and Hossain, 2011;Izadi and Ghalambor, 2013), which makes their use much difficult.…”

Section: Introductionmentioning

confidence: 99%

A new approach in petrophysical rock typing

Mirzaei‐Paiaman

Ostadhassan

Rezaee

et al. 2018

Journal of Petroleum Science and Engineering

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106

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Petrophysical rock typing in reservoir characterization is an important input for successful drilling, production, injection, reservoir studies and simulation. In this study petrophysical rock typing is divided into two major categories: 1) a petrophysical static rock type (PSRT): a collection of rocks having the same primary drainage capillary pressure curves or unique water saturation for a given height above the free water level, 2) a petrophysical dynamic rock type (PDRT): a set of rocks with a similar fluid flow behavior. It was shown that static and dynamic rock types do not necessarily overlap or share petrophysical properties, regardless of wettability. In addition, a new index is developed to define PDRTs via the Kozeny-Carman equation and Darcy's law. We also proposed a different index for delineation of PSRTs by combining the Young-Laplace capillary pressure expression and the Kozeny-Carman equation. These new indices were compared with the existing theoretical and empirical indices. Results showed that our indices are representatives of previously developed models which were also tested with mercury injection capillary pressure, water-oil primary drainage capillary pressure, and water-oil relative permeability data on core plugs from a highly heterogeneous carbonate reservoir in an Iranian oil field. This study enabled us to modify the conventional J-function to enhance its capability of normalizing capillary pressure data universally.

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“…Beside simulation, deduction of the rock types before SCAL tests may be useful in sample selection for this sort of tests. Attempts have been made to use rock types in sample selection process (Mirzaei-Paiaman and Saboorian-Jooybari 2016;Siddiqui et al 2003Siddiqui et al , 2006. Recently, Lian et al (2016) have used the idea of capillary height to build three dimensional map of water saturation.…”

Section: Introductionmentioning

confidence: 99%

Capillary-based method for rock typing in transition zone of carbonate reservoirs

Dakhelpour-Ghoveifel

Shegeftfard

Dejam

2018

J Petrol Explor Prod Technol

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Reservoir simulation is established as a good practice to make the best decision for a petroleum reservoir. The reservoir is characterized in terms of reservoir elements such as structural model, well data, rock and fluid properties. Then the reservoir model is enhanced through history matching and finally different prediction scenarios are tried to find the best plan for the reservoir understudy. The more accurate the reservoir is characterized, the faster and the more precisely the history match is finished and the more reliable predictions are obtained. The most important part of reservoir characterization is the rock typing, where the quality of CCAL (conventional core analysis) and SCAL (special core analysis) properties are evaluated and estimated for any simulation grid. The resulting oil in place must be confirmed by the OOIP (original oil in place) calculated based on average petro-physical parameters for any layer. To allocate different rock types to simulation grid, rock types should be assigned according to different ranges of rock differentiation parameter which has to be determined in any specific study. Based on our experience in Iranian carbonate reservoirs, most frequently irreducible water saturation is the rock differentiation parameter. In the oil zone, water saturation from log data is assumed to be the irreducible water saturation. Thus, the rock type is identified with no trouble. The problem arises in transition zone, where water saturation from log data is not equal to the irreducible water saturation of that rock. This study includes the observed variations in terms of water saturation data versus depth and how to assign rock types to the transition zone grids. The objective of the capillarybased method is to produce a water saturation map which honors laboratory data as well as the well log data and considers the depth so that it can handle the transition zone in a proper manner. In fact, novelty of this work is to explain how it is possible to consider log and capillary pressure data together so that the most accurate rock type is assigned to reservoir grids of the transition zone. Moreover, this method is consistent with equilibration method for initializing reservoir simulations. A procedure is presented for how to implement the capillary-based method in a stepwise manner. Once the proposed method is carried out, the initialized simulation model is consistent with all sources of data (core analysis and petro-physical data). In this procedure original oil in place calculated after the initialization for simulation is more accurate and can be crosschecked with volumetric calculation based on interpreted log data. Therefore, it is considered to facilitate subsequent stages of reservoir study, namely history match and prediction.

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A method based on spontaneous imbibition for characterization of pore structure: Application in pre-SCAL sample selection and rock typing

Cited by 52 publications

References 35 publications

A Novel Energy Lifting Approach Using J-Function and Flow Zone Indicator for Oil Fields

A Novel Energy Lifting Approach Using J-Function and Flow Zone Indicator for Oil Fields

A new approach in petrophysical rock typing

Capillary-based method for rock typing in transition zone of carbonate reservoirs

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