NMR Permeability Calibration using a Non-Parametric Algorithm and Data from a Formation in Central Arabia

Al-Ajmi, Fahad; Holditch, Stephen A.

doi:10.2523/68112-ms

Cited by 6 publications

(4 citation statements)

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“…The optimized model parameters for this case are a = 2, b = 4, and c = 2, with an RMSE value of 2.19 and an R 2 value of 0.6. NMR permeability was also estimated using the SDR model [20] and compared with laboratory measurements. The SDR equation is shown in Equation (6):…”

Section: Nmr Porosity and Permeabilitymentioning

confidence: 99%

Insight into the Pore Characteristics of a Saudi Arabian Tight Gas Sand Reservoir

et al. 2019

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The petrophysical characterization of tight gas sands can be affected by clay minerals, gas adsorption, microfractures, and the presence of high-density minerals. In this study, we conducted various petrophysical, petrographic, and high-resolution image analyses on Saudi Arabian tight sand in order to understand how a complex pore system responds to measurement tools. About 140 plug samples extracted from six wells were subjected to routine core analyses including cleaning, drying, and porosity–permeability measurements. The porosity–permeability data was used to identify hydraulic flow units (HFU). In order to probe the factors contributing to the heterogeneity of this tight sand, 12 subsamples representing the different HFUs were selected for petrographic study and high-resolution image analysis using SEM, quantitative evaluation of minerals by scanning electron microscope (QEMSCAN), and micro-computed tomography (µCT). Nuclear magnetic resonance (NMR) and electrical resistivity measurements were also conducted on 56 subsamples representing various lithofacies. NMR porosity showed good agreement with other porosity measurements. The agreement was remarkable in specific lithofacies with porosity ranging from 0.1% to 7%. Above this range, significant scatters were seen between the porosity methods. QEMSCAN results revealed that samples with <7% porosity contain a higher proportion of clay than those with porosity >7%, which are either microfractured or contain partially dissolved labile minerals. The NMR T2 profiles also showed that samples with porosity <7% are dominated by micropores while samples with porosity >7% are dominated by macropores. Analysis of the µCT images revealed that pore throat sizes may be responsible for the poor correlation between NMR porosity and other porosity methods. NMR permeability values estimated using the Shlumberger Doll Research (SDR) method are fairly correlated with helium permeability (with an R2 of 0.6). Electrical resistivity measurements showed that the different rock types fall on the same slope of the formation factors versus porosity, with a cementation factor of 1.5.

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Section: Nmr Porosity and Permeabilitymentioning

confidence: 99%

Insight into the Pore Characteristics of a Saudi Arabian Tight Gas Sand Reservoir

et al. 2019

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“…However, parametric regressions may yield erroneous and even misleading results when the relationship between the dependent and the independent variables is unknown (Jensen and Lake, 1985;Wendt et al, 1986;Xue et al, 1997). More recently, nonparametric methods based on successive refinements have gained wide popularity in a variety of disciplines ranging from engineering to air and soil pollution control (Steele, 1990;Gordon et al, 1993;Smith, 1994;McCain et al, 1998;Al-Ajmi and Holditch, 2001;Nashawi and Malallah, 2006;2009). The main advantage of these techniques is that they define the regression surface in an iterative fashion while remaining completely data driven as opposed to other model driven correlations.…”

Section: Nonparametric Optimal Transformationsmentioning

confidence: 99%

Prediction of Water Influx of Edge-Water Drive Reservoirs Using Nonparametric Optimal Transformations

2012

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The accurate estimation of water influx into a petroleum reservoir is very important in many reservoir engineering applications, such as material balance calculation, design of pressure maintenance programs, and advanced reservoir simulation studies. These applications have heavily relied on the classical work of van Everdingen and Hurst for finite and infinite edge-water drive reservoirs. However, for both types of water drive reservoirs, the calculation of water influx is not a straight forward task. Table lookup and interpolation between time entries are needed, and furthermore for finite aquifers, interpolation between tables may be also required.The paper presents nonparametric optimal transformations models for the prediction of dimensionless water-influx and dimensionless pressure drop for finite and infinite edge-water drive reservoirs using Graphical Alternating Conditional Expectation (GRACE). In order to achieve maximum efficiency, all the terms involved in the models are used in dimensionless form. GRACE transformations are totally data driven and do not assume any a priori functional form. The results of the various cases are in excellent agreement with the original tables of van Everdingen and Hurst. IntroductionPetroleum reservoirs are often surrounded from the edge or the bottom by water aquifers that support the reservoir pressure through water influx. In response to a pressure drop in the petroleum reservoir, the water aquifer reacts to offset, or retard, pressure decline by providing a source of water influx or encroachment. To determine the effect that an aquifer has on the oil and gas production, it is important to estimate the amount of water that has entered into the reservoir from the aquifer. Such calculation is not a simple and risk-free task due to the involvement of many unknown parameters. For instance, aquifer pressure, thickness, permeability, porosity, shape, and areal extent are usually all unknown variables. Furthermore, water aquifer models are classified according to the flow geometry as either edge-water or bottom-water drive (Figures 1 and 2). These models have completely different flow behavior. The type of the water aquifer, its size, properties, and the amount of water that it can deliver into the reservoir for a certain pressure drop during a specific period of time affect the entire production life of the reservoir. A good knowledge of the aquifer properties, specifically the amount of water that it can provide into the reservoir, dictates the production schedule and the development strategies that need to be implemented in order to optimize oil recovery. Many authors have presented different models for estimating the water influx. These models apply to different flow regimes, including steady-state, modified steady-state (Schilthius, 1963), pseudo-steady-state (Hurst, 19431958;Leung, 1986), and unsteady-state (Fetkovitch, 1971;Leung, 1988). van Everdingen and Hurst (1949) presented the most commonly used water-influx model. This model is basically a solution of the radi...

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“…More recently, nonparametric methods that are based on successive refinements have gained wide recognition in a variety of disciplines ranging from engineering to air and soil pollution control. − The main advantage of these techniques is that they define the regression surface in an iterative fashion while remaining completely “data-driven” as opposed to other “model-driven” correlations.…”

Section: Nonparametric Optimal Transformationsmentioning

confidence: 99%

Accurate Estimation of the World Crude Oil PVT Properties Using Graphical Alternating Conditional Expectation

2006

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The accurate determination of the pressure, volume, and temperature (PVT) properties such as bubble-point pressure and oil formation volume factor is important in the primary and subsequent development of an oil field. These two parameters are essential for all petroleum engineering calculations such as reservoir simulations, recovery estimates, material balance calculations, well completion, facility design decisions, and production optimization strategies. In this study, a new approach is presented for predicting bubble-point pressure and oil formation volume factor for crude oil samples collected from different regions around the world. The regions include major oil-producing fields in North and South America, the North Sea, Southeast Asia, the Middle East, and Africa. The new approach, which is based on nonparametric optimal transformations, is called alternating conditional expectation (ACE). The transformations are totally data-driven and do not assume any a priori functional form. The data set used in the study consists of 5200 points that represent worldwide crude. An additional 200 PVT data sets were used to investigate the effectiveness of the new proposed method to predict outputs from inputs that were not used during the training process. The ACE model is able to predict the bubble-point pressure and oil formation volume factor as a function of the solution gas-oil ratio, the gas relative density, the oil specific gravity, and the reservoir temperature. The excellent results obtained from the proposed model establish a new simple tool for calculation of the two properties. The accuracy of the models developed in this study was compared in detail with several published correlations.

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NMR Permeability Calibration using a Non-Parametric Algorithm and Data from a Formation in Central Arabia

Cited by 6 publications

References 0 publications

Insight into the Pore Characteristics of a Saudi Arabian Tight Gas Sand Reservoir

Insight into the Pore Characteristics of a Saudi Arabian Tight Gas Sand Reservoir

Prediction of Water Influx of Edge-Water Drive Reservoirs Using Nonparametric Optimal Transformations

Accurate Estimation of the World Crude Oil PVT Properties Using Graphical Alternating Conditional Expectation

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