Integrated Characterization and Flow Modeling of a Heterogeneous Carbonate Reservoir in Daleel Field, Oman

2000 ◽  
Vol 3 (02) ◽  
pp. 150-159 ◽  
Author(s):  
Maghsood Abbaszadeh ◽  
Naoki Koide ◽  
Yoya Murahashi

Summary This article presents applications of deterministic and conditional geostatistical reservoir characterization methods to the heterogeneous carbonates of the upper Shuaiba formation in Daleel field, Oman. High-resolution reservoir descriptions based on the integration of logs, core, pressure transient tests, geology, and seismic data are constructed; and upscaled for use in reservoir simulation models to history match field performance data. Generally, geostatistical techniques combined with geology and proper upscaling of permeability heterogeneity yield best results without artificial alterations in various fluid and rock properties. Although acceptable history matches can be obtained with compromised less-detailed reservoir descriptions, these require modifications to reservoir data beyond reasonable ranges. Only detailed and concise reservoir descriptions result in history matches that are consistent with a variety of measured data sources. Introduction Reservoir characterization has gained a new momentum in the past decade, largely due to the introduction of geostatistical methods to the petroleum industry and rapid progress made in their advancement.1 The keen interest in reservoir characterization arises because it is well recognized that reservoir heterogeneity has a profound affect on all phases of hydrocarbon recovery, ranging from oil in-place calculations to sweep and conformance efficiency determination of various injection processes. Thus, any improved understanding of a reservoir will aid in better management and better exploitation of its hydrocarbon recovery potential. The challenge in understanding and predicting reservoir performance is two-fold: first, to describe reservoir geologic heterogeneities realistically and quantitatively, and second to model reservoir flow behavior in the presence of all heterogeneities accurately and efficiently.2 While large-scale reservoir features (such as main layers or major faults) can be described by deterministic techniques, less-correlated medium-scale and more-chaotic small-scale heterogeneities may be characterized by geostatistical methods or related interpolative techniques. This is especially true for estimating interwell reservoir properties based on a limited amount of information available at wells. The approaches to reservoir characterization fall into three categories: deterministic, stochastic, and combination of the two. The deterministic approach has been in use for several decades and ample success with it has been reported. The interwell properties are generally interpolated or extrapolated using algorithms based on the inverse-distance-square principle or variations of it. Usually, adjustments to the number of layers, gridblock properties, relative permeabilities, and even fluid properties are made in order to history match field performance. Some of these adjustments are warranted and some are solely knobs that are arbitrarily tuned in simulation models without physical bases. Thus, the resulting reservoir models may lack reliability and predictive capability. Geostatistical methods, however, generate multiple realizations of reservoir heterogeneity that honor available data, but differ from one another by interwell properties where direct information is not available. The data used in these models are by in large of static nature coming mainly from cores, logs, and seismic attribute extractions. Dynamic information, such as pressure transient tests and production data, are usually excluded from explicit use in geostatistical reservoir characterization, primarily due to difficulty on how to best integrate them a priori into such models. However, recent advances have been made for direct inclusion of this dynamic information through the techniques of simulated annealing3 or direct volume-averaged upscaling.4 Nevertheless, these geostatical reservoir descriptions are capable of capturing detailed geology more realistically and of producing acceptable history matches to field performance data without artificial alterations to various reservoir or fluid properties.5–10 This article applies both methods of deterministic and geostatistical reservoir characterizations to describe and history match the primary recovery performance of a complex carbonate reservoir in Daleel field, Oman. This is a comparative study in an attempt to identify an applicable description method for this field to aid in its exploitation. The deterministic model investigates effects of layering and fluid bubblepoint pressure on production performance. The geostatistical approaches model detailed reservoir heterogeneity and evaluate the importance of proper representation of heterogeneity in flow simulations. During the course of the study, new or alternate approaches for various elements of reservoir characterization techniques have been developed, which are also included. Background Field Description. The reservoir of Daleel field is an elongated carbonate shoal sands and back carbonates in the upper Shuaiba formation. Five geographical sedimentary environments of protected back shoal, shoal, shoal margin slope, inner shelf, and outer shelf comprise the formation. The productive portion of the reservoir is situated in the protected back shoal region (central part of the carbonate mound) and its marginal parts are located in regions with alternating cycles of shoal and shelf sequences. The reservoir is a stratigraphic-structural oil trap accumulation. Bioclastic peloidal packstone and wackstone form the main reservoir sedimentary material in this field. Repeated upward shallowing parasequence cycles, which relate to the geographical sedimentary environment, are recognized on wireline responses. These parasequence boundaries may be considered as synchronous surfaces for interwell correlation. Detailed core and thin section studies have identified 12 lithofacies in the upper Shuaiba, ranging from coarse grain porous limestone to argillaceous lime and lime mudstone. Microstylolites, burrowing and other forms of diagenesis are common. Therefore, pore/throat size distribution and their connectivity as influenced by secondary diagenesis processes mainly control porosity and permeability developments. Significant changes in these lithofacies occur laterally and vertically, and there is an important tightly consolidated discontinuous lime mudstone deposit in the middle of the productive upper zone in the central part of the field.

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Mohammad Reza Kamali ◽  
Azadeh Omidvar ◽  
Ezatallah Kazemzadeh

The aim of geostatistical reservoir characterization is to utilize wide variety of data, in different scales and accuracies, to construct reservoir models which are able to represent geological heterogeneities and also quantifying uncertainties by producing numbers of equiprobable models. Since all geostatistical methods used in estimation of reservoir parameters are inaccurate, modeling of “estimation error” in form of uncertainty analysis is very important. In this paper, the definition of Sequential Gaussian Simulation has been reviewed and construction of stochastic models based on it has been discussed. Subsequently ranking and uncertainty quantification of those stochastically populated equiprobable models and sensitivity study of modeled properties have been presented. Consequently, the application of sensitivity analysis on stochastic models of reservoir horizons, petrophysical properties, and stochastic oil-water contacts, also their effect on reserve, clearly shows any alteration in the reservoir geometry has significant effect on the oil in place. The studied reservoir is located at carbonate sequences of Sarvak Formation, Zagros, Iran; it comprises three layers. The first one which is located beneath the cap rock contains the largest portion of the reserve and other layers just hold little oil. Simulations show that average porosity and water saturation of the reservoir is about 20% and 52%, respectively.


Geofluids ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-19 ◽  
Author(s):  
Miller Zambrano ◽  
Alan D. Pitts ◽  
Ali Salama ◽  
Tiziano Volatili ◽  
Maurizio Giorgioni ◽  
...  

Fluid flow through a single fracture is traditionally described by the cubic law, which is derived from the Navier-Stokes equation for the flow of an incompressible fluid between two smooth-parallel plates. Thus, the permeability of a single fracture depends only on the so-called hydraulic aperture which differs from the mechanical aperture (separation between the two fracture wall surfaces). This difference is mainly related to the roughness of the fracture walls, which has been evaluated in previous works by including a friction factor in the permeability equation or directly deriving the hydraulic aperture. However, these methodologies may lack adequate precision to provide valid results. This work presents a complete protocol for fracture surface mapping, roughness evaluation, fracture modeling, fluid flow simulation, and permeability estimation of individual fracture (open or sheared joint/pressure solution seam). The methodology includes laboratory-based high-resolution structure from motion (SfM) photogrammetry of fracture surfaces, power spectral density (PSD) surface evaluation, synthetic fracture modeling, and fluid flow simulation using the Lattice-Boltzmann method. This work evaluates the respective controls on permeability exerted by the fracture displacement (perpendicular and parallel to the fracture walls), surface roughness, and surface pair mismatch. The results may contribute to defining a more accurate equation of hydraulic aperture and permeability of single fractures, which represents a pillar for the modeling and upscaling of the hydraulic properties of a geofluid reservoir.


2021 ◽  
Author(s):  
Said Beshry Mohamed ◽  
Sherif Ali ◽  
Mahmoud Fawzy Fahmy ◽  
Fawaz Al-Saqran

Abstract The Middle Marrat reservoir of Jurassic age is a tight carbonate reservoir with vertical and horizontal heterogeneous properties. The variation in lithology, vertical and horizontal facies distribution lead to complicated reservoir characterization which lead to unexpected production behavior between wells in the same reservoir. Marrat reservoir characterization by conventional logging tools is a challenging task because of its low clay content and high-resistivity responses. The low clay content in Marrat reservoirs gives low gamma ray counts, which makes reservoir layer identification difficult. Additionally, high resistivity responses in the pay zones, coupled with the tight layering make production sweet spot identification challenging. To overcome these challenges, integration of data from advanced logging tools like Sidewall Magnetic Resonance (SMR), Geochemical Spectroscopy Tool (GST) and Electrical Borehole Image (EBI) supplied a definitive reservoir characterization and fluid typing of this Tight Jurassic Carbonate (Marrat formation). The Sidewall Magnetic resonance (SMR) tool multi wait time enabled T2 polarization to differentiate between moveable water and hydrocarbons. After acquisition, the standard deliverables were porosity, the effective porosity ratio, and the permeability index to evaluate the rock qualities. Porosity was divided into clay-bound water (CBW), bulk-volume irreducible (BVI) and bulk-volume moveable (BVM). Rock quality was interpreted and classified based on effective porosity and permeability index ratios. The ratio where a steeper gradient was interpreted as high flow zones, a gentle gradient as low flow zones, and a flat gradient was considered as tight baffle zones. SMR logging proved to be essential for the proper reservoir characterization and to support critical decisions on well completion design. Fundamental rock quality and permeability profile were supplied by SMR. Oil saturation was identified by applying 2D-NMR methods, T1/T2 vs. T2 and Diffusion vs. T2 maps in a challenging oil-based mud environment. The Electrical Borehole imaging (EBI) was used to identify fracture types and establish fracture density. Additionally, the impact of fractures to enhance porosity and permeability was possible. The Geochemical Spectroscopy Tool (GST) for the precise determination of formation chemistry, mineralogy, and lithology, as well as the identification of total organic carbon (TOC). The integration of the EBI, GST and SMR datasets provided sweet spots identification and perforation interval selection candidates, which the producer used to bring wells onto production.


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