Utilizing Capillary Pressure Measurements and Water Saturation Logs to Determine Reservoir Quality in a Giant Middle Eastern Carbonate Field

Classifying rock facies and estimating permeability is particularly challenging in Microporous dominated carbonate rocks. Reservoir rock types with a very small porosity range could have up to two orders of magnitude permeability difference resulting in high uncertainty in facies and permeability assignment in static and dynamic models. While seismic and conventional porosity logs can guide the mapping of large scale features to define resource density, estimating permeability requires the integration of advanced logs, core measurements, production data and a general understanding of the geologic depositional setting. Core based primary drainage capillary pressure measurements, including porous plate and mercury injection, offer a valuable insight into the relation between rock quality (i.e., permeability, pore throat size) and water saturation at various capillary pressure levels. Capillary pressure data was incorporated into a petrophysical workflow that compares current (Archie) water saturation at a particular height above free water level (i.e., capillary pressure) to the expected water saturation from core based capillary pressure measurements of various rock facies. This was then used to assign rock facies, and ultimately, estimate permeability along the entire wellbore, differentiating low quality microporous rocks from high quality grainstones with similar porosity values. The workflow first requires normalizing log based water saturations relative to structural position and proximity to the free water level to ensure that the only variable impacting current day water saturation is reservoir quality. This paper presents a case study where this workflow was used to detect the presence of grainstone facies in a giant Middle Eastern Carbonate Field. Log based algorithms were used to compare Archie water saturation with primary drainage core based saturation height functions of different rock facies to detect the presence of grainstones and estimate their permeability. Grainstones were then mapped spatially over the field and overlaid with field wide oil production and water injection data to confirm a positive correlation between predicted reservoir quality and productivity/injectivity of the reservoir facies. Core based permeability measurements were also used to confirm predicted permeability trends along wellbores where core was acquired. This workflow presents a novel approach in integrating core, log and dynamic production data to map high quality reservoir facies guiding future field development strategy, workover decisions, and selection of future well locations.

SE Abu Dhabi Aptian 5 Shuaiba System: Understanding a Heterogeneous Reservoir Trend

The study area covers 1,300 km2 in southeastern Abu Dhabi and focuses on the Aptian (Apt.) 5 Upper Shuaiba progradational clinoform system. The Shuaiba Formation has been well-studied at the regional level, but with comparatively less focus on the Apt. 5 system. Studying depositional trends and shoal facies distributions within the Apt. 5 is critical for predicting reservoir presence and quality. Given the complexity of the Apt. 5 system, understanding the key controls over depositional environments, such as paleowind direction, is an important first step. This study combined regional context and geological understanding with previous studies to confirm existing clinoform interpretation, while also delineating four additional clinoform sequences using a reprocessed depth migrated 3-D seismic volume. Isochron maps were also used to group clinoforms into three packages distinguished by common morphologies possibly linked to their respective dominant reservoir facies. Preliminary observations suggest early clinoforms had more rudist build-ups, whereas the later clinoforms were dominated by narrow-shoal beaches. Coalescing clinoform shoal patterns, observed in the spectral decomposition and amplitude extraction maps, likely result from a combination of Bab Basin morphology, longshore current, and dominant paleowind direction during the Early to Middle Cretaceous. Existing interpretations of dominant paleowind direction vary significantly, ranging between E-W and S-N. Interpretations from this study are most consistent with prevailing paleowind out of the east-southeast. The Arabian plate was likely near the equator around 10°S latitude during the Aptian, which supports the southeast wind hypothesis when considering modern Coriolis patterns. Consistent wind influence on shallow water shoal environments would have winnowed mud and increased the proportion of grain-dominated sediment preserved relative to lower energy areas. The grain-dominated facies appear to be reflected in amplitude responses around the coalescing clinoforms, and in the amplitude variations along strike coincident with clinoform edges. Reservoir presence and quality uncertainty can be reduced if these observations can be confirmed. An improved understanding of the Apt. 5 clinoform system in southeast Abu Dhabi, and possible influences on reservoir distribution and quality, will help develop a better understanding of risk for prospect maturation.

Detailed Lithological Study Leads to New Insights about the Pre-Tertiary and Tertiary Reservoirs in the Suban Gas Field, South Sumatra Basin, Indonesia

The basement igneous intrusive rock lithology in the South Sumatra Basin was previously suggested to be a solely granitic rock. It is also a common knowledge that the Miocene Baturaja Formation carbonates are one of the prolific reservoirs. However, after a comprehensive reservoir recharacterization had been conducted in the Suban Field, new insights regarding these two rock types were revealed. The basement lithology consists of a more complex metasediment containing Andesite, Granodiorite, and Gabbro and an Oligocene-age carbonate reservoir was also identified. The reservoir recharacterization was carried out by conducting an integrated analysis to reconstruct the complex reservoir configuration utilizing seismic data, core, cuttings, absolute age dating, and biostratigraphy. Seismic data was utilized as a general framework for reservoir architecture due to the resolution that allowed to describe the reservoir configuration in detail. Core and cuttings were used to identify the reservoir facies, and absolute age dating along with biostratigraphy were used to construct geochronology for each reservoir facies. Finally, well to well correlation was performed to reconstruct complex reservoir configurations. The result of the study indicated that the reservoir age in the field can be divided into two parts, pre-Tertiary (PRT) basement and Tertiary sediments. The PRT of Suban Field comprises several types of crystalline rock that will have different respond to the stresses and the Tertiary section that consists of clastic and several carbonate facies of different ages that vary across the study area. This study offers new insights regarding the basement configuration and the emerging carbonate play. Different igneous rock compositions reflect a complex magmatism process in South Sumatra. Oligocene carbonates that were identified in Suban could open the opportunity to discover a hydrocarbon-bearing Oligocene carbonate play in the South Sumatra Basin.

An Improved Method of Reservoir Facies Modeling Based on Generative Adversarial Networks

As the reservoir and its attribute distribution are obviously controlled by sedimentary facies, the facies modeling is one of the important bases for delineating the area of high-quality reservoir and characterizing the attribute parameter distribution. There are a large number of continental sedimentary reservoirs with strong heterogeneity in China, the geometry and distribution of various sedimentary microfacies are relatively complex. The traditional geostatistics methods which have shortage in characterization of the complex and non-stationary geological patterns, have limitation in facies modeling of continental sedimentary reservoirs. The generative adversarial network (GANs) is a recent state-of-the-art deep learning method, which has capabilities of pattern learning and generation, and is widely used in the domain of image generation. Because of the similarity in content and structure between facies models and specific images (such as fluvial facies and the images of modern rivers), and the various images generated by GANs are often more complex than reservoir facies models, GANs has potential to be used in reservoir facies modeling. Therefore, this paper proposes a reservoir facies modeling method based on GANs: (1) for unconditional modeling, select training images (TIs) based on priori geological knowledge, and use GANs to learn priori geological patterns in TIs, then generate the reservoir facies model by GANs; (2) for conditional modeling, a training method of “unconditional-conditional simulation cooperation” (UCSC) is used to realize the constraint of hard data while learning the priori geological patterns. Testing the method using both synthetic data and actual data from oil field, the results meet perfectly the priori geological patterns and honor the well point hard data, and show that this method can overcome the limitation that traditional geostatistics are difficult to deal with the complex non-stationary patterns and improve the conditional constraint effect of GANs based methods. Given its good performance in facies modeling, the method has a good prospect in practical application.

Optimized algorithm for multipoint geostatistical facies modeling based on a deep feedforward neural network

Reservoir facies modeling is an important way to express the sedimentary characteristics of the target area. Conventional deterministic modeling, target-based stochastic simulation, and two-point geostatistical stochastic modeling methods are difficult to characterize the complex sedimentary microfacies structure. Multi-point geostatistics (MPG) method can learn a priori geological model and can realize multi-point correlation simulation in space, while deep neural network can express nonlinear relationship well. This article comprehensively utilizes the advantages of the two to try to optimize the multi-point geostatistical reservoir facies modeling algorithm based on the Deep Forward Neural Network (DFNN). Through the optimization design of the multi-grid training data organization form and repeated simulation of grid nodes, the simulation results of diverse modeling algorithm parameters, data conditions and deposition types of sedimentary microfacies models were compared. The results show that by optimizing the organization of multi-grid training data and repeated simulation of nodes, it is easier to obtain a random simulation close to the real target, and the simulation of sedimentary microfacies of different scales and different sedimentary types can be performed.

Improved gas sand facies classification and enhanced reservoir description based on calibrated rock physics modelling: A case study

Rock physics provides a dynamic tool for quantitative analysis by developing the basic relationship between fluid, lithological, and depositional environment of the reservoir. The elastic attributes such as impedance, density, velocity, V p/V s ratio, Mu-rho, and Lambda-rho are crucial parameters to characterize reservoir and non-reservoir facies. Rock physics modelling assists like a bridge to link the elastic properties to petrophysical properties such as porosity, facies distribution, fluid saturation, and clay/shale volume. A robust petro-elastic relationship obtained from rock physics models leads to more precise discrimination of pay and non-pay facies in the sand intervals of the study area. The Paleocene aged Lower Ranikot Formation and Pab sandstone of Cretaceous age are proven reservoirs of the Mehar gas field, Lower Indus Basin. These sands are widely distributed in the southwestern part of the basin and are enormously heterogeneous, which makes it difficult to distinguish facies and fluid content in the reservoir intervals. So, an attempt is made in this paper to separate the reservoir facies from non-reservoir facies by using an integrated approach of the petro-elastic domain in the targeted sand intervals. Furthermore, missing logs (S-sonic and P-sonic) were also synthesized in the wells and missing intervals along with improving the poor quality of the density log by captivating the washouts and other side effects. The calibrated rock physics model shows good consistency between measured and modelled logs. Petro-elastic models were predicted initially using petrophysical properties and incorporated at true reservoir conditions/parameters. Lithofacies were defined based on petrophysical cut-offs. Rock physics modelled elastic properties (Lambda-rho versus Mu-rho, impedance versus V p/V s ratio) were then cross-plotted by keeping lithofacies in the Z-axis. The cross-plots clearly separated and demarcated the litho-fluid classes (wet sand, gas sand, shale, and limestone) with specific orientation/patterns which were randomized in conventional petrophysical analysis.