This paper presents a multidomain integrated workflow that combines geophysics, borehole geology, fracture modeling, and petroleum systems analysis for characterization and resource assessment of basement plays. A 3D fracture model is developed by integrating image log interpretation and seismic data to assess the reservoir potential of fractured basement. The 3D fracture modeling is done using the discrete fracture network (DFN) approach with image log interpretation and other fracture drivers serving as the main input. The DFN is upscaled to generate fracture porosity and fracture permeability properties in a 3D grid. The upscaled fracture porosity is used to estimate the petroleum initially in place (PIIP) for the prospects. Multiple 2D petroleum system modeling is performed where large fault throws are identified from seismic interpretation. The petroleum system study helps in identification of areas with most prolific hydrocarbon generation and expulsion centers, which, coupled with the cross-fault juxtapositions, are the main locales of charging for basement reservoir. Further analysis of all the elements of basement play (i.e., source, reservoir, seal, trap, and migration) is done, and prospective areas within the basement play are delineated with high geological chance of success.
Identification of a prospect is normally done based on seismic interpretation and geological understanding of the area. However, due to the inherent uncertainties of the data we still observe in many cases that all key petroleum system elements are present, but still the drilled prospect is dry. Such failures are mostly attributed to a lack of understanding of seal capacity, reservoir heterogeneity, source rock presence and maturation, hydrocarbon migration, and relative timing of these processes. The workflow described in this paper aims to improve discovery success rates by deploying a more rigorous and structured approach. It is guided by the play-based exploration risk assessment process. The starting point is always that the process is guided by the the basic understanding of a mature kitchen should always be based on a regional scale petroleum systems model. However, while evaluating prospects, the migration and entrapment component of a prospect should always be investigated by means of a locally refined grid-based petroleum system model. The uniquepart of this approach is the construction of a high-resolution static model covering the prospects, which is built by using available well data, seismo-geological trends and attributes to capture reservoir potential. Additional inputs such as fault seal analysis also helps to understand prospect scale migration and associated geological risks. In the regional play and local prospect-scale petroleum system models, geological and geophysical inputs are utilized to create the uncertainty distribution for each input parameter which is required for assessing the success case volume of identified prospects. The evaluated risk is combined with the volumetric uncertainty in a probabilistic way to derive the risked volumetrics. It is further translated into an economic evaluation of the prospect by integrating inputs like estimated production profiles, appropriate fiscal models, HC price decks, etc. This enables the economic viability of the prospects to be assessed, resulting in a portfolio with proper ranking to build a decision-tree leading to execution and operations in ensuing drilling campaigns.
Fatehgarh reservoirs in Aishwariya field, located in Barmer Basin of Rajasthan India, have very high CO2 content in reservoir fluid. A procedure was developed earlier to model the impact of reservoir CO2 on waterflood, polymer flood and ASP flood (
The objective of this work was to validate the modelling procedure developed to predict the produced gas rate in such a system with very high amount of CO2 in reservoir fluid.
A live oil coreflood experiment was carried out using 12 inches long Bentheimer core under Aishwariya reservoir pressure and temperature conditions. After saturating the core with live oil, the core was water flooded with brine for ~3.7 pore volumes. Produced gas volume was measured at different times so as to generate gas production profile.
Two different simulation techniques were used to simulate the experiment and match the gas production profile. First technique was using a compositional simulator with EOS based PVT while the other technique was using an "advanced processes simulator" modeling the component distributions based on partitioning coefficients. Both methods could successfully capture the production of gas from both liquid streams; oil and water and a reasonable match for the produced gas could be obtained.
The approach developed to simulate impact of CO2 on different aqueous based flooding processes in Aishwariya field was validated by matching the coreflood experiment carried out under actual Aishwariya reservoir conditions. It helped to confirm confidence in performance prediction of aqueous based flooding mechanisms planned in Aishwariya field despite the presence of significant amount of CO2.
The paper presents history match of unconventional produced gas profile of a coreflood carried out under Aishwariya field conditions with very high amount of dissolved CO2. The proposed method can be applied to estimate produced gas rate in other fields with very high amount of CO2 in reservoir fluid.
Seunghwan Baek and I. Yucel Akkutlu, Texas A&M University Summary Source rocks, such as organic-rich shale, consist of a multiscale pore structure that includes pores with sizes down to the nanoscale, contributing to the storage of hydrocarbons. In this study, we observed hydrocarbons in the source rock partition into fluids with significantly varying physical properties across the nanopore-size distribution of the organic matter. This partitioning is a consequence of the multicomponent hydrocarbon mixture stored in the nanopores, exhibiting a significant compositional variation by pore size-- the smaller the pore size, the heavier and more viscous the hydrocarbon mixture becomes. The concept of composition redistribution of the produced fluids uses an equilibrium molecular simulation that considers organic matter to be a graphite membrane in contact with a microcrack that holds bulk-phase produced fluid. A new equation of state (EOS) was proposed to predict the density of the redistributed fluid mixtures in nanopores under the initial reservoir conditions. A new volumetric method was presented to ensure the density variability across the measured pore-size distribution to improve the accuracy of predicting hydrocarbons in place. The approach allowed us to account for the bulk hydrocarbon fluids and the fluids under confinement. Multicomponent fluids with redistributed compositions are capillary condensed in nanopores at the lower end of the pore-size distribution of the matrix ( 10 nm). The nanoconfinement effects are responsible for the condensation. During production and pressure depletion, the remaining hydrocarbons become progressively heavier. Hence, hydrocarbon vaporization and desorption develop at extremely low pressures. Consequently, hydrocarbon recovery from these small pores is characteristically low. Introduction Resource shale and other source-rock formations with significant amounts of organic matter, such as mudstone, siltstone, and carbonate, have a multiscale pore structure that includes fractures, microcracks, and pores down to a few nanometers (Ambrose et al. 2012; Loucks et al. 2012). The total amount of hydrocarbons stored is directly proportional to the amount of organic matter.
Mancilla-Polanco, Adel (University of Calgary) | Johnston, Kim (University of Calgary) | Richardson, William D. L. (University of Calgary) | Schoeggl, Florian F. (University of Calgary) | Zhang, Y. George (University of Calgary) | Yarranton, Harvey W. (University of Calgary) | Taylor, Shawn D. (Schlumberger-Doll Research)
The phase behavior of heavy-oil/propane mixtures was mapped from temperatures ranging from 20 to 180°C and pressures up to 10 MPa. Both vapor/liquid (VL1) and liquid/liquid (L1L2) regions were observed. Saturation pressures (VL1 boundary) were measured in a Jefri 100-cm3 pressure/volume/temperature (PVT) -cell and blind-cell apparatus. The propane content at which a light propane-rich phase and a heavy bitumen-rich (or pitch) phase formed (L1/L1L2 boundary) was visually determined with a high-pressure microscope (HPM) while titrating propane into the bitumen. High-pressure and high-temperature yield data were measured using a blind-cell apparatus. Here, yield is defined as the mass of the indicated component(s) in the pitch phase divided by the mass of bitumen in the feed. A procedure was developed and used to measure propane-rich-phase and pitch-phase compositions in a PVT cell.
Pressure/temperature and pressure/composition phase diagrams were constructed from the saturation-pressure and pitch-phase-onset data. High-pressure micrographs demonstrated that, at lower temperatures and propane contents, the pitch phase appeared as glassy particles, whereas at higher propane contents and temperatures, it appeared as a liquid phase. Ternary diagrams were also constructed to present phase-composition data. The ability of a volume-translated Peng-Robinson cubic equation of state (CEOS) (Peng and Robinson 1976) to match the experimental measurements was explored. Two sets of binary-interaction parameters were tested: temperature-dependent binary-interaction parameters (SvdW) and composition-dependent binary-interaction parameters (CDvdW). Models derived from both types of binary-interaction parameters matched the saturation pressures and the L1L2 boundaries at one pressure but could not match the pressure dependency of the L1L2 boundary or the measured L1L2 phase compositions. The SvdW model could not match the yield data, whereas the CDvdW model matched yields at temperatures up to 90°C.
Hydrocarbon-reservoir-performance forecasting is an integral component of the resource-development chain and is typically accomplished using reservoir modeling, by means of either numerical or analytical methods. Although complex numerical models provide rigorous means of capturing and predicting reservoir behavior, reservoir engineers also rely on simpler analytical models to analyze well performance and estimate reserves when uncertainties exist. Arps (1945) empirically demonstrated that certain reservoirs might decline according to simple, exponential, hyperbolic, or harmonic relationships; such behavior, however, does not extend to more-complex scenarios, such as multiphase-reservoir depletion. Because of this limitation, an important research area for many years has been to transform the equations governing flow through porous media in such a way as to express complex reservoir performance in terms of closed analytical forms. In this work, we demonstrate that rigorous compositional analysis can be coupled with analytical well-performance estimations for reservoirs with complex fluid systems, and that the molar decline of individual hydrocarbon-fluid fractions can be expressed in terms of rescaled exponential equations for well-performance analysis. This work demonstrates that, by the introduction of a new partial-pseudopressure variable, it is possible to predict the decline behavior of individual fluid constituents of a variety of gas/condensate-reservoir systems characterized by widely varying richness and complex multiphase-flow scenarios. A new four-region-flow model is proposed and validated to implement gas/condensate-deliverability calculations at late times during variable-bottomhole-pressure (BHP) production. Five case studies are presented to support each of the model capabilities stated previously and to validate the use of liquid-analog rescaled exponentials for the prediction of production-decline behavior for each of the hydrocarbon species.
An accurate description of the microemulsion-phase behavior is critical for many industrial applications, including surfactant flooding in enhanced oil recovery (EOR). Recent phase-behavior models have assumed constant-shaped micelles, typically spherical, using netaverage curvature (NAC), which is not consistent with scattering and microscopy experiments that suggest changes in shapes of the continuous and discontinuous domains. On the basis of the strong evidence of varying micellar shape, principal micellar curves were used recently to model interfacial tensions (IFTs). Huh’s scaling equation (Huh 1979) also was coupled to this IFT model to generate phase-behavior estimates, but without accounting for the micellar shape.
In this paper, we present a novel microemulsion-phase-behavior equation of state (EoS) that accounts for changing micellar curvatures under the assumption of a general-prolate spheroidal geometry, instead of through Huh’s equation. This new EoS improves phase-behavior-modeling capabilities and eliminates the use of NAC in favor of a more-physical definition of characteristic length. Our new EoS can be used to fit and predict microemulsion-phase behavior irrespective of IFT-data availability. For the cases considered, the new EoS agrees well with experimental data for scans in both salinity and composition. The model also predicts phase-behavior data for a wide range of temperature and pressure, and it is validated against dynamic scattering experiments to show the physical significance of the approach.
Significant research has been conducted on hydrocarbon fluids in the organic materials of source rocks, such as kerogen and bitumen. However, these studies were limited in scope to simple fluids confined in nanopores, while ignoring the multicomponent effects. Recent studies using hydrocarbon mixtures revealed that compositional variation caused by selective adsorption and nanoconfinement significantly alters the phase equilibrium properties of fluids. One important consequence of this behavior is capillary condensation and the trapping of hydrocarbons in organic nanopores. Pressure depletion produces lighter components, which make up a small fraction of the in-situ fluid. Equilibrium molecular simulation of hydrocarbon mixtures was carried out to show the impact of CO2 injection on the hydrocarbon recovery from organic nanopores. CO2 molecules introduced into the nanopore led to an exchange of molecules and a shift in the phase equilibrium properties of the confined fluid. This exchange had a stripping effect and, in turn, enhanced the hydrocarbon recovery. The CO2 injection, however, was not as effective for heavy hydrocarbons as it was for light components in the mixture. The large molecules left behind after the CO2 injection made up the majority of the residual (trapped) hydrocarbon amount. High injection pressure led to a significant increase in recovery from the organic nanopores, but was not critical for the recovery of the bulk fluid in large pores. Diffusing CO2 into the nanopores and the consequential exchange of molecules were the primary drivers that promoted the recovery, whereas pressure depletion was not effective on the recovery. The results for N2 injection were also recorded for comparison.
Compositional simulators conventionally use Li's correlation to approximate the critical temperature (
We here propose to replace Li's correlation by a rigorous calculation of
In order to address the observation that gas/oil relative permeability curves tend to straight lines when approaching to the critical point, a second level of interpolation with respect to the IFT is applied within the phase envelope between miscible and immiscible three-phase models. Continuity is, by construction, guaranteed at any possible phase-state transition.
The proposed relative permeability model is first tested standalone (i.e., on a single cell) with different hydrocarbon mixtures, by analysis of the dependent parameter (true or fictitious IFT) and the relative permeabilities at different
The model is secondly implemented in our In-House Research Reservoir Simulator (IHRRS), and tested on a synthetic 2D cross-section undergoing near-critical gas injection. We observe that with conventional models based on Li's correlation, discontinuities in the relative permeability model when crossing the phase envelope occur, as well as spurious phase flipping. No such unphysical behavior is observed with the proposed approach, while requiring the same input data.
There is of course a computational cost involved in properly calculating
In this work, we present the development of a compositional simulator accelerated by proxy flash calculation. We aim to speed up the compositional modeling of unconventional formations by stochastic training.
We first developed a standalone vapor-liquid flash calculation module with the consideration of capillary pressure and shift of critical properties induced by confinement. We then developed a fully connected network with 3 hidden layers using Keras. The network is trained with Adam optimizer. 250,000 samples are used as training data, while 50,000 samples are used as testing data. Based on the trained network, we developed a forward modeling (prediction) module in a compositional simulator. Therefore, during the simulation run, the phase behavior of the multicomponent system within each grid block at each iteration is obtained by simple interpolation from the forward module.
Our standalone flash calculation module matches molecular simulation results well. The accuracy of the trained network is up to 97%. With the implementation of the proxy flash calculation module, the CPU time is reduced by more than 30%. In the compositional simulator, less than 2% of CPU time is spent in the proxy flash calculation.
The novelty of this work lies in two aspects. We have incorporated the impacts of both capillary pressure and shift of critical properties in the flash calculation, which matches molecular simulation results well. We developed a proxy flash calculation module and implemented it in a compositional simulator to replace the traditional flash calculation module, speeding the simulation by 30%.