Comparative study of models for predicting permeability from nuclear magnetic resonance (NMR) logs in two Chinese tight sandstone reservoirs

Tight reservoir permeability with values ranging from a few nD-0.1 mD is a challenging parameter to measure. Since the 1970s, many correlations were applied to estimate the permeability of tight formations using the nuclear magnetic resonance (NMR) technique. Due to increasing interest in tight reservoir recovery, each year many papers are published on the Timur-Coates and Kenyon models and their modification on estimating the NMR permeability of tight reservoirs. It brings up the question of which is the most accurate, reliable, and feasible model to be used for predicting tight NMR permeability.The first part of this paper is dedicated to introducing the existing models for NMR-based estimates of tight reservoir permeability. The second part compares these models by applying them on tight reservoirs from North America, Asia, and Europe. NMR experiments were conducted on 150 cores. NMR T 2 relaxation times were measured and effective and total porosities, the geometric mean of relaxation time (T 2gm ), irreducible bulk volume (BVI), and free fluid index (FFI) were calculated. For validation of the models, the cross plots of NMR permeability versus the Klinkenberg gas permeability are presented. Comparison of the model is based on the calculated standard error of these two independently measured permeabilities.This paper is beneficial in understanding existing tight reservoir NMR permeability models. The results can be used as a guide for choosing the best NMR permeability estimation model for tight reservoirs data.

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“…A comprehensive study on estimating Timur‐Coates permeability using mercury injection capillary pressure (MICP) can be found in the literature …”

Section: Resultsmentioning

confidence: 99%

Application of nuclear magnetic resonance permeability models in tight reservoirs

Solatpour

Kantzas

2019

Can J Chem Eng

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“…Before performing the data selection for all wells, the basic statistics and histograms for reservoir parameters were calculated (Table 2). Basic statistics of laboratory data indicate that the studied formations exhibit very poor reservoir parameters, in particular absolute permeability, which is typical for unconventional hydrocarbon resources, such as tight gas or shale gas (Xiao et al 2014). For the training well A, the absolute permeability is very low, mostly not exceeding 0.01 mD, as evidenced by the median, i.e.…”

Section: Resultsmentioning

confidence: 96%

Estimation of absolute permeability using artificial neural networks (multilayer perceptrons) based on well logs and laboratory data from Silurian and Ordovician deposits in SE Poland

2019

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Permeability is a property of rocks which refers to the ability of fluids to flow through each substance. It depends on several factors as pore shape and diameter. Also the presence and type of clay has a large influence on the permeability value. Permeability can be measured on rock sample in the laboratory by injecting fluid through the rock under known condition, but this provides only point information. Due to the dependence of the parameter on many factors, the deterministic estimation of permeability based on laboratory measurement and well logs is problematic. Many empirical methods for determining permeability are available in the literature and interpretation systems. An interesting approach to the problem is the use of artificial neural networks based on laboratory measurement and modern, high-resolution logging tools. The authors decided to use MLP artificial neural networks, which allow permeability estimation and can be used both in the test well and applied to neighbouring wells. The network was checked in several variants. Obtained results show the legitimacy of using artificial neural networks in the issue of estimating permeability. However, they also show limitations resulting from the lack of accurate data or influence of geological setting and processes.

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“…It is called as the Swanson parameter and expressed as (S Hg /P c ) max . Xiao et al [19] verified that the Swanson parameter is highly correlated with permeability in conventional and low permeability sandstones, and permeability estimation models based on the Swanson parameter can be established, whereas in tight gas sands, the relationship between the Swanson parameter and permeability is not always exist because the MICP curve is not always a hyperbolic curve.…”

Section: Calibration Of M and N In The Target Tight Gas Sandsmentioning

confidence: 99%

Tight Gas Sandstone Reservoirs Evaluation from Nuclear Magnetic Resonance (NMR) Logs: Case Studies

2015

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Tight gas sandstone reservoirs parameters, such as porosity, permeability and initial water saturation, are difficult to be precisely estimated from conventional logs. What's more, the effective gas-bearing formations cannot be directly identified either due to the characteristics of complicated pore structure, strong heterogeneity and high irreducible water saturation. Nuclear magnetic resonance (NMR) logs, which are usually used to evaluate reservoir pore structure, are found to be effective in evaluating tight gas sandstone reservoirs. In this study, typical tight gas sandstone reservoirs of southwest China are used as examples; techniques of estimating porosity, permeability, initial water saturation and constructing pseudo-capillary pressure curve to quantitative evaluate tight sandstone reservoirs pore structure are studied. The acoustic and NMR logs are combined to calculate porosity. The technique proposed by Volokitin et al. (1999) is used to construct pseudo-capillary pressure curves from NMR logs.The saturation-height-function method is used to estimate initial water saturation, and the Swanson parameter based model is established to calculate permeability from constructed pseudo-capillary pressure curves. Comparisons of estimating porosity, permeability and water saturation with core-derived results illustrate that these techniques are effective in tight gas sands evaluation. Finally, the effective tight gas sands can be identified through combined use of the estimated reservoir parameters and constructed pseudo-capillary pressure curves from NMR logs, which is verified by the drill stem test data.

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Comparative study of models for predicting permeability from nuclear magnetic resonance (NMR) logs in two Chinese tight sandstone reservoirs

Cited by 45 publications

References 13 publications

Application of nuclear magnetic resonance permeability models in tight reservoirs

Application of nuclear magnetic resonance permeability models in tight reservoirs

Estimation of absolute permeability using artificial neural networks (multilayer perceptrons) based on well logs and laboratory data from Silurian and Ordovician deposits in SE Poland

Tight Gas Sandstone Reservoirs Evaluation from Nuclear Magnetic Resonance (NMR) Logs: Case Studies

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