Rock-pore-space geometry and network topology have a great impact on dynamic reservoir characteristics, in particular on capillary pressure and relative permeability curves. Hydraulic tortuosity is a key independent measurement relating the pore-space geometry and topology to the rock's effective porosity and absolute permeability. Therefore, hydraulic tortuosity can be an important concept for dynamic reservoir characterization and reservoir simulation. Our objectives are to recommend a new dynamic rock-typing process and to assess the corresponding improvement on reservoir simulation processes. We introduce an innovative dynamic reservoir-rock-typing (DRRT) index, using absolute permeability, porosity and hydraulic tortuosity data, derived from mercury-injection capillary pressure (MICP) experiments. For correlation purposes, we also derived electric tortuosity data from formation-resistivity experiments. We used the experimental data from the Worldwide Rock Catalog (WWRC) provided by a joint-industry project (Core Lab, 2014), for both carbonate and clastic rocks. Based on the new proposed DRRT index and on corresponding dynamic reservoir properties, we prepared a comprehensive sensitivity study on the impact of hydraulic tortuosity heterogeneity on oil recovery results. This sensitivity study was done by incorporating the concept of hydraulic tortuosity in a synthetic carbonate- reservoir simulation model. The analysis of the MICP and formation-resistivity data showed both greater average tortuosity and greater tortuosity variability for carbonates, when compared with clastic rocks. It also showed good correlation between hydraulic and electric tortuosity values. The sensitivity study results showed a significant impact of hydraulic tortuosity heterogeneity on oil in place and reserves estimates for improved oil recovery (IOR) / enhanced oil recovery (EOR) processes in typical complex carbonate reservoirs, such as the ones found in the Brazilian Pre-Salt. It also showed the importance of applying proper corrections while deriving dynamic reservoir properties from capillary pressure and relative permeability experiments. The new DRRT index shows a much stronger correlation with pore-space geometry when compared with traditional reservoir-quality (RQI) and flow-zone (FZI) indexes. Therefore, it has clear potential to enhance the dynamic rock-typing process for reservoir simulation of IOR / EOR in complex carbonate rocks. We also discuss the importance of an integrated laboratory test and well log program to enable the proper characterization, population, and upscaling of dynamic rock properties. In complex carbonate reservoirs under IOR / EOR, overlooking the rock-pore-space geometry and network topology may result in significant errors in reservoir characterization and simulation processes. In this context, proper DRRT in carbonates, including tortuosity, is therefore crucial for reservoir simulation; enabling correspondence between core, well log and reservoir-scale dynamic properties. The presented correlation between hydraulic and electric tortuosity significantly increases the potential of dielectric measurements for dynamic reservoir characterization of complex carbonates on both core and well log scales.

Relative permeability is a key input for multiphase reservoir simulations. Challenges related to the validation and interpretation of the laboratory core measurements are associated with the restoration processes and resulting wettability states, the heterogeneities and multi-scale aspects of complex rocks, as well as the limitations of core flooding experiments. Moreover, the relative permeability curves from several samples can be scattered and their correlation with wettability and reservoir-rock types not directly apparent. Grouping, averaging, end-point scaling and assessing the data uncertainty are crucial steps in relative permeability data processing. To improve these processes, a new workflow is proposed, based on the water fractional flow concept (fw), which is an effective representation of the behavior of oil displacement by water injection, combining both relative permeability curves to oil and water into a single, equivalent curve. First, the water fractional flow curve, obtained from a relative permeability core flooding test, is normalized according to saturation end-points at a constant viscosity ratio equal to one. Such normalization allows the separation of the fw plot area into two regions according to the wettability state of the samples. Fractional flow curves for the same sample but at opposite wettability conditions, i.e. strongly oil-wet or water-wet, present a remarkable symmetry, from which a wettability index is calculated. This proposed new wettability index may then be compared to other indexes like Amott-Harvey or USBM for validation. Additionally, the shape of the normalized water fractional flow curves is influenced by rock-pore sizes. Subsequently, the normalized fractional flow curves are grouped by wettability and by reservoir-rock type, supporting the validation of the relative permeability data and identifying associated trends and uncertainties. The average, lower and upper-bound normalized fractional flow curves are obtained for each group. Likewise, relative-permeability and saturation end-points are correlated with reservoir-rock-type index or other rock properties. Finally, average, lower and upper-bound normalized relative permeability sets of curves and corresponding end-points may be used for reservoir simulation. Alternatively, de-normalized relative permeability curves can be obtained. By varying wettability, a controlled relative permeability dataset is obtained using direct-hydrodynamic (DHD) simulations on 3D digital rock model of a carbonate core sample. The proposed workflow is applied to such a dataset. The results confirm the ability of the method to correctly identify the different wetting states and to group the fractional flow curves accordingly. The proposed wettability index, directly obtained from relative permeability data, may be complementary to other industry wettability indexes and better represent the expected displacement behavior. The proposed workflow, although simple and widely applicable, considerably improves the relative permeability analysis process. It can be integrated with other core analysis, well-log analysis and digital-rock analysis workflows.

Irreducible water saturation is a key property for the estimation of original oil and gas-in-place. It is also key to end-point scaling of capillary pressure and relative permeability, with significant impact on simulation results of reservoirs under improved/enhanced oil recovery (IOR/EOR). Several definitions of irreducible water saturation exist, based on different experimental measurements and standard estimation methods. We propose a comprehensive model and a new method for improved estimation of irreducible water saturation. The model considers rock wettability; the thin film of water that coats portions of the rock grains; the pore size distribution; the tortuosity; and the ratio between pore-throat and pore-body sizes (BTR). Different components of the irreducible water saturation are identified for multimodal, heterogeneous rocks: a nano-porosity system completely filled with water and other pore systems with their walls coated by water. The model also considers an additional residual water saturation resulting from laboratory experimental limits as the maximum applied pressure and duration. The method adjusts the model parameters by fitting to a set of irreducible saturation data, obtained from both mercury injection (MICP) and air-brine drainage capillary pressure experiments. The method estimates the irreducible water saturation for the asymptotic ideal condition - very high capillary pressure and reservoir geological times – as well as for other laboratory and reservoir conditions. We applied the proposed method to experimental data from Corelab's worldwide rock catalog. The fraction of nano-porosity not revealed by MICP experiment was estimated by comparing MICP porosity with routine effective porosity. Hydraulic tortuosity and truncated multi-Gaussian decomposition of pore-throat-size distribution were also obtained from MICP data. BTR range was estimated from NMR data, thin sections, and hydraulic tortuosity data. Water thin film thickness range was estimated from the literature. Model parameters were then successfully estimated using data from 49 carbonate and 106 clastic samples from all over the world. The results showed that, in several cases, the asymptotic irreducible water saturation might be significantly smaller than the observed value from the air-brine experiment. Therefore, the corresponding reservoir irreducible water saturation could also be overestimated. The relative importance of the different components of the irreducible water saturation varied from one sample to the other, confirming the relevance and completeness of the proposed method. When compared to traditional methods, the proposed method significantly improves irreducible water saturation estimates, resulting in better saturation-height and end-point scaling functions, and more accurate reserves. It is particularly important for simulation of IOR/EOR processes. The method may also be integrated with dielectric and NMR well log measurements, increasing the resolution of dynamic reservoir characterization, with particular importance to mixed-wet rock environments.

This paper was selected for presentation by an OTC program committee following review of information contained in an abstract submitted by the author (s). Contents of the paper have not been reviewed by the Offshore Technology Conference and are subject to correction by the author (s). The material does not necessarily reflect any position of the Offshore Technology Conference, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Offshore Technology Conference is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of OTC copyright. topology may result in significant deviations in the quality of reservoir characterization and simulation results. In this context, proper multi-Gaussian decomposition and the introduction of a new multi-Gaussian universal J** -function are therefore crucial for carbonate reservoir simulations.The proposed truncated multi-Gaussian pore-throat-size decomposition presents significant additional benefits when compared to Thomeer's method. It also improves dynamic-reservoir-rock-typing and reservoir simulation processes. The new universal J** -function can be used to reconstruct capillary pressure curves from the information provided by multi-Gaussian pore-throat-size decomposition. Therefore, the new concepts presented in this paper have a clear potential to enhance the simulation of IOR and EOR in complex carbonate and clastic reservoirs.

During produced water reinjection into an oilfield, the formation near the wellbore is progressively damaged due to total suspended solids (TSS) and oil particles in the injected water (OIW). This typically increases the bottom-hole injection pressure over time. Furthermore, if the water is injected in the oil zone, the initial bottom-hole injection pressure may already be high from the start due to water mobility constraint and oil viscosity. This study aims to model the generation of hydraulic fractures induced under different conditions, their geometrical characteristics and corresponding development over time. Such information is key to reservoir simulation for the secondary oil recovery and to reservoir integrity assessment. Four disciplines are integrated into the proposed workflow: reservoir flow simulation, formation damage modeling, reservoir geomechanics, and the simulation of hydraulic fracturing. First, a sector model around an injector well is extracted from the full-field reservoir simulation of the case-study reservoir. In the reservoir flow simulation, a formation damage model is implemented, calibrated from injection rate, bottom-hole pressure, TSS and OIW actual data. At specified time steps, the flow simulator passes pore pressure profiles of the sector model to the geomechanical simulator, which computes the corresponding changes in stress and deformation. The updated in-situ stress field, in combination with the petrophysical model applied for the flow simulation, is provided to the hydraulic fracturing simulator, which tests for the development of the hydraulic fracture and computes its geometry. The resulting hydraulic fracture is mapped back into the reservoir flow model to account for the local increase of permeability of the cells hosting the fracture. The workflow then enters into a loop starting again with the flow simulation, and the further development of the fracture under changing conditions is tested and modeled. The proposed workflow was successfully applied to an injection well in an offshore field. Four scenarios considered different initial formation saturation, injected fluid viscosity and the conversion of a producing well into an injector. Multiple fractures with different characteristics, fully contained inside the reservoir, were predicted for each scenario and gave insights into the hydraulic fracture development during produced water reinjection. The proposed method and workflow have the potential to significantly improve the reservoir simulation of the water injection process for secondary recovery or pressure maintenance by providing insights into how induced fracture geometries will influence the injection pressure and reservoir sweep efficiency. It also may provide valuable information to assess the integrity of reservoir cap rock during produced water reinjection.

Oil-water relative permeability and capillary pressure are key inputs for multiphase reservoir simulations. These data are significantly impacted by the wettability state in the reservoir and by the pore space characteristics of the rock. However, in the laboratory, there are several challenges related to the validation and interpretation of the special core analysis (SCAL) measurements. They are mostly associated with the core preservation or restoration processes and resulting wettability states. To improve dynamic reservoir rock typing (DRRT) process, a new model, describing the change of wettability fraction with depth in mixed-wet reservoirs, is proposed. The proposed model is based on solid physics describing the interactions between the rock grain surfaces and the fluids filling the pore space. First, the model considers the oil migration from the source rock into the originally water-wet reservoir and the corresponding capillary pressure rise, as the height above the free water level (HAFWL) is progressively increased. Then, oil-wet and water-wet fractions are estimated for different static reservoir rock types (SRRT) and different HAFWL, based on the wettability change potential of the rock-fluid system and oil-water capillary pressure curves. Additionally, mixed-wet capillary pressure and relative permeability curves are estimated for both oil displacing water (drainage) and water displacing oil (imbibition) processes, based on the estimated mixed-wet fractions and single-wet curves. We discussed the model assumptions and its parameters’ uncertainties. We prepared a comprehensive sensitivity study on the impact of wettability variability with depth on oil recovery results. This study used a synthetic carbonate-reservoir simulation model, under waterflooding, by incorporating the concept of DRRT defined according to the different SRRT and estimated wettability fractions. The results showed a significant impact of wettability variability on oil in place and reserves estimates for waterflooding processes in typical complex, mixed-wet carbonate reservoirs, such as the ones found in the Brazilian Pre-Salt. We also discuss the potential impact of wettability change with depth on well logs like resistivity, nuclear magnetic resonance (NMR) and dielectric logs. The proposed reservoir wettability model and its corresponding DRRT workflow is relatively simple and widely applicable, and may significantly improve reservoir simulation and wettability uncertainty analysis. It also explicitly identifies the required wettability parameters to be obtained from laboratory experiments and well logs. Finally, the proposed model may be integrated with special core analysis, well logs and digital-rock analysis.