Improved Permeability Estimates in Carbonate Reservoirs Using Electrofacies Characterization: A Case Study of the North Robertson Unit, West Texas
- Trond Mathisen (Texas A&M U.) | Sang Heon Lee (Texas A&M U.) | Akhil Datta-Gupta (Texas A&M U.)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Evaluation & Engineering
- Publication Date
- June 2003
- Document Type
- Journal Paper
- 176 - 184
- 2003. Society of Petroleum Engineers
- 5.1.2 Faults and Fracture Characterisation, 5.5.2 Core Analysis, 4.1.5 Processing Equipment, 5.4.1 Waterflooding, 1.14 Casing and Cementing, 4.1.2 Separation and Treating, 5.1 Reservoir Characterisation, 5.8.7 Carbonate Reservoir, 1.6.9 Coring, Fishing, 5.1.3 Sedimentology, 5.5 Reservoir Simulation, 5.6.1 Open hole/cased hole log analysis, 1.2.3 Rock properties
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We propose a simple, cost-effective approach to obtaining permeability estimates in heterogeneous carbonate reservoirs using commonly available well logs. Our approach follows a two-step procedure. First, we classify the well-log data into electrofacies types. This classification does not require any artificial subdivision of the data population but follows naturally based on the unique characteristics of well-log measurements, reflecting minerals and lithofacies within the logged interval. A combination of principal component analysis (PCA), model-based cluster analysis (MCA), and discriminant analysis is used to identify and characterize electrofacies types. Second, we apply nonparametric regression techniques to predict permeability using well logs within each electrofacies.
Our proposed method has been successfully applied to the North Robertson Unit (NRU) in Gaines county, west Texas. Previous attempts to derive permeability correlations at the NRU have included rock-type identification using thin-section and pore-geometry analysis that can sometimes be expensive and time-consuming. The proposed approach resulted in improved permeability estimates, leading to an enhanced reservoir characterization, and can potentially benefit both daily operations and reservoir simulation efforts. The successful field application demonstrates that the electrofacies classification used in conjunction with sound geologic interpretation can significantly improve reservoir descriptions in complex carbonate reservoirs.
Permeability estimates are a critical aspect of a reservoir description. In sandstone reservoirs, a linear relationship normally exists between porosity and the logarithm of permeability. Thus, permeability predictions in sandstones can be achieved with acceptable accuracy using porosity from well logs. In carbonates, however, petrophysical variations rooted in diagenesis, grain-size variation, and cementation can significantly alter the direct relationship between porosity and permeability.1 Statistical regression has been proposed as a more versatile solution to the problem of permeability estimation. Conventional statistical regression generally has been done parametrically with multiple linear or nonlinear models.2-4
Several limitations inhibit multiple regression techniques, many arising from the inexact nature of the relationship between petrophysical variables. Conventional parametric regression requires a priori assumptions regarding functional relationships between the independent and dependent variables. In complex carbonate reservoirs, such underlying physical relationships are not known in advance, making traditional multiple regression techniques inadequate and often leading to biased estimates.5,6
A variety of approaches have been proposed to partition welllog responses into distinct classes to improve permeability predictions. The simplest approach uses flow zones or reservoir layering. 4,6 Other approaches have used lithofacies information identified from cores, as well as the concept of hydraulic flow units (HFUs).7-11 However, in carbonate reservoirs, such classification is complicated by the extreme petrophysical variations rooted in diagenesis and complex pore geometry, even within a single zone or class. A major difficulty in this regard has been discrimination of classes from well logs in uncored wells.12,13
This paper discusses the application of a simple and cost-effective approach to estimating permeability in a complex carbonate reservoir based on a two-step approach. The first step involves classification of well-log data into electrofacies types. By electrofacies, we imply a "rock type" or sediment exhibiting a similar set of well-log responses.14 This classification does not require any artificial subdivision of the data but is based on the unique characteristics of well-log measurements, reflecting minerals and lithofacies within the interval. A combination of PCA, MCA, and discriminant analysis is used to describe and identify the electrofacies types. The electrofacies classification can be improved further by incorporating geologic interpretation. Then, a nonparametric regression technique, the Alternating Conditional Expectation (ACE) algorithm, is used to develop permeability correlations using well logs within each electrofacies. Such techniques are completely data-driven and do not require a priori assumptions regarding functional forms for correlating permeability and well logs. In a previous paper,12 we have discussed the application of this method to the Salt Creek Field Unit in the Permian Basin, west Texas. In this paper, we focus on the North Robertson Unit (NRU) in Gaines County, west Texas. The producing horizons in the NRU are the Glorieta and Clearfork formations, often referred to as the Upper and Lower Clearfork. The hydrocarbon-bearing interval extends from the top of the Glorieta to the base of the Lower Clearfork, between the correlative depths of approximately 5,874 and 7,440 ft.
Previous attempts8,9 to derive permeability correlations at the NRU have had mixed results. This includes rock-type identification using thin sections and pore-geometry analysis, a procedure that can be expensive and time-consuming. We present a simple, cost-effective, and computationally efficient approach for permeability estimation using only commonly available well logs. For the examples studied here, our approach appears to result in better permeability predictions, leading to an enhanced reservoir characterization based on flow, permeability (rather than storage), or porosity. This can have potential benefits both in daily operations and in reservoir simulation efforts. Our successful field applications also demonstrate the power and versatility of electrofacies characterization in improving reservoir descriptions in complex carbonate reservoirs.
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