Carbonate reservoir characterization and fluid quantification seem more challenging than those of sandstone reservoirs. The intricacy in the estimation of accurate hydrocarbon saturation is owed to their complex and heterogeneous pore structures, and mineralogy. Traditionally, resistivity-based logs are used to identify pay intervals based on the resistivity contrast between reservoir fluids. However, few pay intervals show reservoir fluids of similar resistivity which weaken reliance on the hydrocarbon saturation quantified from logs taken from such intervals. The potential of such intervals is sometimes neglected. In this case, the studied reservoir showed low resistivity. High water saturation was estimated, while downhole fluid analysis identified mobile oil, and the formation produced dry or nearly dry oil. Because of the complexity of Lowresitivity pay (LRP) reservoirs, its cause should be determined a prior to applying a solution. Several reasons were identified to be responsible for this phenomenon from the integration of thin section, nuclear magnetic resonance (NMR) and mercury injection capillary pressure (MICP) data-among which were the presence of microporosity, fractures, paramagnetic minerals, and deep conductive borehole mud invasion. In this paper, we integrated various information coming from geology (e.g., thin section, X-ray diffraction (XRD)), formation pressure and well production tests, NMR, MICP, and Dean-Stark data. We discussed the observed variations in quantifying water saturation in LRP interval and their related discrepancies. The nonresistivitybased methods, used in this study, are Sigma log, capillary pressure-based (MICP, centrifuge, and porous plate), and Dean-Stark measurements. The successful integration of these saturation estimation methods captured the uncertainty and improved our understanding of the reservoir properties. This enhanced our capability to develop a robust and reliable saturation model. This model was validated with data acquired from a newly drilled appraisal well, which affirmed a deeper free water level as compared to the previous prognosis, hence an oil pool extension. Further analysis confirmed that the major causes of LRP in the studied reservoir were the presence of microporosity and high saline mud invasion. The integration of data from these various sources added confidence to the estimation of water saturation in the studied reservoir and thus improved reserves estimation and generated reservoir simulation for accurate history matching, production forecasting, and optimized field development plan.

Characterization and fluid quantification of Carbonate reservoirs looks more challenging than those of sandstone reservoirs. The determination of accurate hydrocarbon saturation is more tasking due to their complex and heterogeneous pore structures, and mineralogy. Traditionally, resistivity logs are used to identify pay intervals due to the resistivity contrast between oil and water. However, when pay intervals exhibit low resistivity, such logs exhibit low confidence in the precise determination of the hydrocarbon saturation. Few Middle-Eastern reservoirs are categorized as low resistivity pay, where resistivity based log analysis results in high water saturation. However, downhole fluid analysis identifies mobile oil, and the formation flows dry or nearly dry oil during production tests. This makes resistivity based saturation computation questionable. Because of the complexity of low resistivity pay (LRP), its cause should be determined prior to applying a solution. Several reasons were identified to be responsible for this phenomenon- among which are the presence of micro-porosity, fractures, paramagnetic minerals, and deep conductive borehole mud invasion. Integration of Thin section, Nuclear magnetic resonance (NMR) and Mercury injection capillary pressure (MICP) data from the studied formation indicated the presence of micropores network. This paper discusses the observed variations in quantifying water saturation in LRP interval and the related discrepancies between the resistivity and non-resistivity based techniques. The non-resistivity based methods, used in the course of this study, are coined from sigma log measurement and core data, either capillary pressure-based (MICP, Centrifuge, and Porous plate), or direct from Dean-Stark measurements. The interpretation process considered water saturation derived from resistivity measurement and core data combined with production test information. The combination of several water saturation determination approaches captured the uncertainty and improved our understanding of the reservoir properties. This enhanced our capability to develop a robust and reliable saturation model. The integration of data from these various sources added confidence to the estimation of water saturation in the studied field and thus, improved reserves estimation and reservoir simulation for accurate history matching, production forecasting and optimized field development plan.

Early identification of low resistivity pay (LRP) reservoir is vital in assessing its prospect and capability. Productive reservoirs may exhibit low resistivity and consequently, their potential is simply overlooked. Remapping these intervals can have significant production and reserve implications. Traditionally, resistivity logs are used to identify pay intervals due to the resistivity contrast between oil and formation water. However, when pay intervals exhibit low resistivity, such logs return low confidence in defining hydrocarbon potential. Due to the complexity of low resistivity pay (LRP), its cause and proper mitigation should be determined prior to applying a solution. Researchers have identified several reasons responsible for this occurrence; among which are the presence of heterogeneous pore structures specifically micro-porosity, fractures, paramagnetic minerals, and deep conductive mud invasion. Almost all preceding publications assume a technique will work but not the other. However, this is the first time, to our knowledge; an integrated approach is used to develop LRP assessment workflow. We have integrated the information coming from geology (e.g., thin-section, XRD), formation pressure and well tests, NMR, MICP, and dean stark data. The integration successfully identified and remapped the carbonate low resistivity reservoir. This model was validated in an appraisal well on Abu Dhabi mainland, for that an extended data was acquired. Thereafter, the integrated LRP model was compared with the computed water saturation from conventional resistivity tools. The validation was successful in terms of confirming the prognosis. Interpreting the results from the multidisciplinary integrated model confirms a deeper Free Water Level (FWL), hence oil pool extension. Further analysis showed that the causes of LRP in this considered formation was limited to presence of micro-porosity and high saline mud invasion.

The concept of uncertainty, risk, and probabilistic assessment is increasingly employed as a standard in the E&P industry to assist in optimum development and investment decisions. The studied Onshore Abu Dhabi field is a Cretaceous complex carbonate oil producing reservoir, which has more than 15 years of production history. This paper discusses an integrated static and dynamic workflow to create a range of probabilistic simulation models to forecast oil production under several production schemes. The study deals with quantitative identification and ranking of factors affecting volumetric and reserves uncertainty in the field. In order to quantify the uncertainties, the main uncertainty parameters and their respective ranges were first identified, selected, and analyzed using Experimental Design to generate a tornado plot which enables the selection of the most influential parameters on the objective function. Secondly was to build a Proxy Model that would help in defining the full probabilistic volumetric distribution on the stock tank oil initial in place (STOIIP) and Recovery Factor (RF). Five main static uncertainty parameters were selected to assess the STOIIP distribution namely structure, free water level, saturation height function, porosity, and formation volume factor. In addition, four dynamic uncertainty parameters were incorporated for reserves estimation specifically Sorw, Kv/Kh, relative permeability, and subdense layer communication. A cumulative distribution function was created in order to extract the probability cases of P10, P50 and P90 of the STOIIP. The simulation models were then built using the P50 volumetric case derived from the static model that was run with hundreds of realizations. Combinations of dynamic uncertainty parameters were simulated using Monte Carlo to define the Low, Base, and High Cases. This was done by comparison with material balance computations and streamline simulation. A stochastic combination of the STOIIP distribution and the RF sensitivities was done through an Experimental Design, Proxy Model, and Monte Carlo approaches. The Base Case model history-match was checked against the choice of parameters defining the Low and High sensitivity cases. The match data available included: oil rates, water cuts, GOR, WHP, flowing and static Pressure, and saturation profiles derived from open and/or cased hole logs. The sensitivity assessment showed that using currently available data, the two major factors affecting the volumetric uncertainty are the free water level and structure. In contrast, porosity possesses the smallest impact. In addition, Kv/Kh and relative permeability are the two main parameters affecting the RF. A number of appraisal wells will be drilled to reduce the structure uncertainty specifically in the flank areas, which will lead to further maturation of reserves. Economic calculations were performed to check that all projects pertaining to the reserves category would consider oil price, CAPEX profile, OPEX profile, well and facility life time.

The energy industry, including the new focus on geothermal and carbon sequestration processes, deals with porous and permeable formations. Under the influence of effective stress, these formations undergo elastic and inelastic deformation, fracturing, and failure, including porosity and permeability changes during production. Grain and Bulk moduli of elasticity are two key parameters that define net effective stress due to partitioning of stresses between the pore pressure and grain-to-grain contact stresses. Effective stress explains poroelastic behavior; however, tight rock behavior under in-situ conditions is still not predictable. This paper proposes a new method, which uses formation evaluation (FE) measurements, and an integration of rock physics and geomechanics concepts, to constrain effective stress in tight rocks. Examples are presented demonstrating the usefulness of the work. Effective stress (σ′) is expressed as the difference between total applied stress (σ) and pore pressure multiplied by Biot’s coefficient (α). The ‘α’ for highly porous rocks is unity where applied load is counteracted equally by grain-matrix and pore-pressure. However, for tight rocks, only a fraction of load is shared by pore fluid and the ‘α’ is much smaller than unity. Biot’s coefficient ‘α’ is expressed in terms of bulk modulus (Kb) and matrix modulus (Kma). Kb is estimated from acoustic logs as well as measured by hydrostatic compression tests in the laboratory. However, Kma is much more difficult to measure safely and economically, especially in tight or very low permeable formations, and as such, the common practice is to estimate it theoretically. A simple and clear methodology is proposed to estimate Kma from FE logs as well asX-RayDiffraction (XRD) mineralogy obtained from formation core and drill cuttings. Kma can be constrained by an upper-bound (Voigt, 1910), a lower- bound (Reuss, 1929), and an average of the two, (Hill, 1963) models. Kb, on the other hand, can be reliably estimated using dynamic acoustic wave velocity and the static equivalents calculated during calibrations from core tests under net effective in-situ stress conditions. The Kma and Kb, thus obtained, will give a good estimate of Biot’s coefficient ‘α’ in tight rocks. The work provides an improved estimate of net- effective-stress in tight rocks, which leads to safety and cost savings through better prediction of drilling rates, hydraulic fracture design and production decline. The work also examines a new method in which Kma could be estimated by weight fraction of minerals.