Sun, Qian (Petroleum Engineering, Texas A&M University at Qatar) | Zhang, Na (Division of Sustainable Development, College of Science and Engineering, Hamad Bin Khalifa University) | Alyafei, Nayef (Petroleum Engineering, Texas A&M University at Qatar) | Wang, Yuhe (Petroleum Engineering, Texas A&M University at Qatar) | Fadlelmula, Mohamed (Petroleum Engineering, Texas A&M University at Qatar)
Reservoir simulation is commonly performed on upscaled models of complex geological models. The upscaling process introduces a principal challenge in accurately simulating two-phase fluid dynamics in porous media. To tackle this challenge, it is important to upscale relative permeability accurately. In this paper, a numerical method, which is based on the mimetic finite difference method (MFD) and digital rock analysis (DRA), is proposed for relative permeability upscaling. The validation of MFD is tested by two different cases with exact pressure solution. Then, the relative permeability of the digital rock (small element) is calculated based on the pore network modeling. The small elements are combined together to make up a larger model with different sizes (4×4×4, 6×6×6, 8×8×8, 10×10×10 elements). Finally, the accuracy of the proposed method is verified by comparing simulated results of the different sizes with that of the original one. The results show that MFD can solve the multi-phase flow scenarios with high accuracy and the
Bigoni, Francesco (Eni S.p.A) | Pirrone, Marco (Eni S.p.A) | Trombin, Gianluca (Eni S.p.A) | Vinci, Fabio Francesco (Eni S.p.A) | Raimondi Cominesi, Nicola (ZFOD) | Guglielmelli, Andrea (ZFOD) | Ali Hassan, Al Attwi Maher (ZFOD) | Ibrahim Uatouf, Kubbah Salma (ZFOD) | Bazzana, Michele (Eni Iraq BV) | Viviani, Enea (Eni Iraq BV)
The Mishrif Formation is one of the important carbonate reservoirs in middle, southern Iraq and throughout the Middle East. In southern Iraq, the formation provides the reservoir in oilfields such as Rumaila/West Qurna, Tuba and Zubair. The top of the Mishrif Formation is marked by a regional unconformity: a long period of emersion in Turonian (ab. 4.4 My) regionally occurred boosted by a warm humid climate, associated to heavy rainfall. In Zubair Field, within the Upper interval of Mishrif Formation, there are numerous evidences of karst features responsible of important permeability enhancements in low porosity intervals that are critical for production optimization and reservoir management purposes.
In the first phase, the integration of Multi-rate Production logging and Well Test analysis was very useful to evaluate the permeability values and to highlight the enhanced permeability (largely higher than expected Matrix permeability) intervals related to karst features; Image log analysis, on the same wells, allowed to find out a relationship between karst features and vug densities, making possible to extend the karst features identification also in wells lacking of well test and Production logging information. This approach has allowed to obtain a Karst/No Karst Supervised dataset for about 60 wells.
In the second phase different seismic and geological attributes have been considered in order to investigate possible correlations with karst features. In fact there are some parameters that show somehow a correlation with Karst and/or NoKarst wells: the Spectral Decomposition (specially 10 and 40 Hz volumes), the detection of sink-holes at top Mishrif on the Continuity Cube and its related distance, the sub-seismic Lineaments (obtained from Curvature analysis and subordinately from Continuity), distance from Top Mishrif. In the light of these results, the most meaningful parameters have been used as input data for a Neural Net Process ("Supervised Neural Network") utilizing the Supervised dataset both as a Trained dataset (70%) and as a Verification dataset (30%). A probability 3D Volume of Karst features was finally obtained; the comparison with verification dataset points out an error range around 0.2 that is to say that the rate of success of the probability Volume is about 80%.
The final outcomes of the workflow are karst probability maps that are extremely useful to guide new wells location and trajectory. Actually, two proof of concept case histories have demonstrated the reliability of this approach. The newly drilled wells, with optimized paths according to these prediction-maps, have intercepted the desired karst intervals as per the subsequent image log interpretation, which results have been very valuable in the proper perforation strategy including low porous intervals but characterized by high vuggy density (Karst features). Based on these promising results the ongoing drilling campaign has been optimized accordingly.
A particular challenge inherent to carbonate reservoirs is reservoir rock typing which impacts model initialisation and saturation distributions and hence STOIIP, phase mobilities, and flow behaviours. We explore how flow diagnostics can be used best to detect subtle differences in reservoir dynamics arising from different model initialisations by comparing flow diagnostics simulations with full-physics simulations.
Flow diagnostics are applied to two reservoirs, a synthetic but realistic model representing an analogue for the Arab-D formation and a giant carbonate reservoir from the Middle East. Saturation modelling and reservoir rock typing is based on uniform and heterogeneous Pc and kr distributions, and further employs a state-of-the-art software that integrates of SCAL data and log-derived saturations. Sweep efficiency and dynamic Lorenz coefficients are then derived from the flow diagnostics results to quantify and compare the dynamic behaviour of the reservoir models. The full-physics simulations, which are used to validate the flow diagnostics results, are carried out with a commercial Black Oil simulator.
The flow diagnostics results can clearly distinguish between different homogenous and heterogeneous rock-type distributions, wettability trends, as well as novel saturation modelling approaches that use dedicated software tools. Flow diagnostics capture the same trends in recovery predictions as the full-physics simulations. Importantly though, the total CPU time for a single flow diagnostics calculation including model loading is on the order of seconds, compared to minutes and hours for a single full-physics simulation. These observation give confidence that flow diagnostics can be used effectively to compare and contrast the impact of reservoir rock typing, saturation modelling, and model initialisation on reservoir performance before running full-physics simulations. Flow diagnostic hence allow us to reduce the number of reservoir models from a model ensemble and select a small number of diverse yet realistic reservoir models that capture the full range of geological uncertainties which are then subjected to more detailed reservoir simulation studies.
Flow diagnostics are particularly well suited for complex carbonate reservoirs which are geologically more complex than clastic reservoirs and often exhibit significant uncertainties. Giant carbonate reservoirs are also challenging to simulate using full-physics simulators due to their size, so the impact of geological uncertainty on the predicted reservoir performance is often underexplored. Flow diagnostics are hence an effective complement to quantify uncertainty in state-of-the-art reservoir modelling, history matching and optimisation workflows, particularly for giant carbonate reservoirs.
This paper discusses the re-construction of the long-term development plan for an offshore giantfield located in Abu Dhabi with the aim to mitigate the rising challenges in the maturing field. The primary objective is to understand the reservoir behavior in terms of fluid movement incorporating the learning from the vast history while correlating with the geological features.
The field has been divided into segments based on multiple factors considering the static properties such as facies distribution, diagenesis, faults, and fractures while incorporating the dynamic behaviors including pressure trends and fluid movements.
On further analysis, various trends have been identified relating these static and dynamic behaviors. The production mechanism for each of the reservoirs and the subsequent sub reservoirs were analyzed with the help of Chan plots, Hall plots and Lorentz plots which distinctly revealed trends that further helped to classify the wells into different production categories.
Using the above methodology the field has been categorized in segments and color coded to indicate areas of different ranking. The green zone indicates area of best interest which currently has strong pressure support and wells can be planned immediately. The wells in this area are expected to produce with a low risk of water and gas. The yellow zone indicates areas of caution where special wells including smart wells maybe required to sustain production. This area showed relatively lower pressure support owing the location of the water injectors and the degraded facies quality between the injectors and the producers. The red zone highlights areas which are relatively mature compared to the neighboring zones and will require new development philosophy to improve the recovery. The findings from this study were used as the basis for a reservoir simulation study using a history matched model, to plan future activities and improve the field recovery.
This study involved an in-depth analysis incorporating the latest findings with respect to the static and dynamic properties of the reservoir. This has helped to classify the reservoir based on the development needs and will play a critical role in designing the future strategies in less time.
The traditional definition of volumetric sweep efficiency sums the effects of both fingering (arising due to contrasts in mobility) and bypassing (arising due to contrasts in permeability as well as well placement). Accordingly, we cannot quantitatively attribute poor sweep to either bypassing or fingering. Similarly, in EOR, the incremental recovery cannot be quantitatively associated with the reduction of those effects. For such purposes, we rely on visualization and mapping of saturation profiles to quantify and characterize the remaining oil in place including its distribution. . In this work, we propose a complementary method to obtain an instantaneous insight of the remaining oil distribution. We demonstrate the decomposition of fingering and bypassing effects and its utility. We first redefine recovery factors such that we decouple bypassing and fingering effects. We then validate those redefined sweep indicators by examining a 5-spot waterflood and two idealistic polymer floods. Later, we demonstrate the possible utility of those redefined sweep indicators through different examples. In one example, we compare the performance of a shear - thinning polymer to a recovery-equivalent Newtonian polymer. Using fingering and bypassing sweep indicators, we can immediately conclude that the shear-thinning polymer exacerbates bypassing. We recommend the adoption of our redefined sweep indicators in any simulation suite. They provide instant understanding of sweep and hence can be complementary to standard practices of oil saturation mapping and of special value when analyzing the results of multiple realizations and/or development scenarios.
This paper presents a diagnostic workflow to understand and implement rock and fluid modeling in a diagenetically heterogeneous and hydrodynamically pressured Middle East carbonate field. The workflow allows interactive field data integration, provides guidance for reservoir property distribution and fluid contact generation in order to improve reserves and forecasting estimation. The workflow is useful to a reservoir modeler in QA/QC role and in this case it proves particularly applicable in an organization with constrained resources during the farm-in process. The workflow runs on numerical methods within the static model to avoid database discrepancy during the diagnostic process. Using the core (CCAL, SCAL), log and pressure database, the geoscientist can assess subsurface modeling outputs from the simplest to more complex deterministic scenarios. The process aims to minimize the discrepancy between data input and model output while continuously honoring the data, maintaining realistic correlations (e.g. between static permeability and water saturation) and respecting inherent uncertainty.
Using a data-rich Middle East carbonate reservoir, the pre- and post-diagnostic comparison of 3D modeled reservoir properties to the input data are demonstrated. Diagnostic steps have helped to understand potential subsurface scenarios and thus minimize the discrepancy post exercise. The value of the workflow is its ability to pinpoint the key uncertainties in rock and fluid modeling from the field’s vast dataset in a shorter diagnostic time. The application of the workflow in this carbonate reservoir case study increases the importance of geological and property driven rock type classification and its 3D distribution in matching the water saturation profile. This proved particularly challenging in this case study due to the field’s compartmentalization - fluid contact scenario.
Bhushan, Yatindra (ADNOC Onshore) | Ali Al Seiari, Reem (ADNOC Onshore) | Igogo, Arit (ADNOC Onshore) | Hashrat Khan, Sara (ADNOC Onshore) | Al Mazrouei, Suhaila (ADNOC Onshore) | Al Raeesi, Muna (ADNOC Onshore) | Al Tenaiji, Aamna (ADNOC Onshore)
A reservoir simulation study has been performed to assess the enhanced oil recovery benefits for a proposed pilot on Simultaneous Injection of Miscible Gas (CO2) and Polymer (SIMGAP) in a giant carbonate reservoir (B) in Abu Dhabi. The model has been used to carry out uncertainty analysis for various input parameters and analyze their impact on pilot performance. The paper discusses the uncertainty analysis in detail.
Reservoir-B consists of B_Upper and B_Lower layers which are in full hydrodynamic equilibrium. However, in the southern and western parts of the reservoir, the B_Upper layer has permeabilities that are one to two orders of magnitude higher than the B_Lower layer. The reservoir is on plateau production under waterflooding, however, it is observed that there is water override in B_Upper. The B_Upper layer is being waterflooded very efficiently, while the B_Lower layer remains largely unflooded and forms the key target for enhanced oil recovery (EOR).
The proposed SIMGAP pilot plans to inject polymer into the B_Upper layer and CO2 into the B_Lower layer with producers in the B_Lower layer. The pilot will utilize a line drive pattern at 250m spacing using 3000 ft horizontal wells. There will be two central horizontal injectors (one in B_Upper and the other in B_Lower) and two horizontal producers (one on either side of the central injectors).
Pilot uncertainty analysis cases have been run by varying different parameters that could impact the pilot performance. The parameters that have been varied are polymer viscosity, polymer adsorption, residual resistance factor, thermal stability of polymer, residual oil to miscible flooding (Sorm), residual oil to water flooding (Sorw), Krw end point, high perm streaks, fracture possibility and extension to B_Upper or B_Lower layers, three phase oil relative permeability models, maximum trapped gas saturation, dense zone permeability and pore volume uncertainty. In addition, a grid sensitivity study was undertaken to test the sensitivity of the process to varying levels of dispersion. The results suggest that the key uncertainties which have impact on recovery are polymer viscosity, polymer adsorption, residual oil saturation to water and CO2, presence of high perm streaks and maximum trapped gas saturation values. Vertical observation wells located between the injector and producer wells (equivalent to 0.3 to 0.4 PV of CO2 injection in B_Lower), will be used to confirm whether the SIMGAP process has been successful in containing CO2 in the B_Lower layer and thereby suppressing crossflow.
Al-Jenaibi, Faisal (ADNOC - Upstream) | Shelepov, Konstantin (Rock Flow Dynamics) | Kuzevanov, Maksim (Rock Flow Dynamics) | Gusarov, Evgenii (Rock Flow Dynamics) | Bogachev, Kirill (Rock Flow Dynamics)
The application of intelligent algorithms that use clever simplifications and methods to solve computationallycomplex problems are rapidly displacing traditional methods in the petroleum industry. The latest forward-thinking approaches inhistory matching and uncertainty quantification were applied on a dynamic model that has unknown permeability model. The original perm-poro profile was constructed based on synthetic data to compare Assisted History Matching (AHM)approach to the exact solution. It is assumed that relative permeabilities, endpoints, or any parameter other than absolute permeability to match oil/water/gas rates, gas-oil ratio, water injection rate, watercut and bottomhole pressure cannot be modified.
The standard approach is to match a model via permeability variation is to split the grid into several regions. However, this process is a complete guess as it is unclear in advance how to select regions. The geological prerequisites for such splitting usually do not exist. Moreover, the values of permeability and porosity in different grid blocks are correlated. Independent change of these values for each region distortscorrelations or make the model unphysical.
The proposed alternative involves the decomposition of permeability model into spectrum amplitudes using Discrete Cosine Transformation (DCT), which is a form of Fourier Transform. The sum of all amplitudes in DCT is equal to the original property distribution. Uncertain permeability model typically involves subjective judgment, and several optimization runs to construct uncertainty matrix. However, the proposed multi-objective Particle Swarm Optimization (PSO) helps to reduce randomness and find optimal undominated by any other objective solution with fewer runs. Further optimization of Flexi-PSO algorithm is performed on its constituting components such as swarm size, inertia, nostalgia, sociality, damping factor, neighbor count, neighborliness, the proportion of explorers, egoism, community and relative critical distance to increase the speed of convergence. Additionally, the clustering technique, such as Principal Component Analysis (PCA), is suggested as a mean to reduce the space dimensionality of resulted solutions while ensuring the diversity of selected cluster centers.
The presentedset of methodshelps to achieve a qualitative and quantitative match with respect to any property, reduce the number of uncertainty parameters, setup ageneric and efficient approach towards assisted history matching.
This work presents a new open access carbonate reservoir case study that uniquely considers the major uncertainties inherent to carbonate reservoirs using one of the most prolific aggradational parasequence carbonate formation set in the U.A.E; the Late Barremian Upper Kharaib Mb. as an analogue. The ensemble considers a range of interpretational scenarios and geomodelling techniques to capture the main components of its reservoir architectures, stratal geometries, facies, pore systems, diagenetic overprints and wettability variations across its platform-to-basin profile.
Fully anonymized data from 43 wells across 22 fields in the Bab Basin, U.A.E from different geo-depositional positions and height above FWL’s (specified to capture multiple structural positions) within an area of 36,000 km2 was used. The data comprises of a full suite of open hole logs and core data which has been anonymized, rescaled, repositioned and structurally deformed; FWL’s were normalized and the entire model was placed in a unique coordinate system. Our petrophysical model captures the geological setting and reservoir heterogeneities of selected fields but now at a manageable scale.
The novelty of this work has been to create semi-synthetic
This paper has an objective of identifying the nature of formation fluid from an extreme tight fractured reservoir. A good understanding of petrophysical properties of the reservoir rock as well as the fluid it contains constitutes a real challenge for tight reservoirs, that are the most common unconventional sources of hydrocarbons. The front-line characterization mean is the Wireline logging which comes directly after drilling the well or while drilling, knowing that for low to extreme low porosity-permeability reservoirs any attempt of conventional well testing will not bring any added value not rather than a confirmation of reservoir tightness. A tailored workflow was adopted to design the most appropriate formation testing module, select the best depths for fluid sampling, and distinguish hydrocarbon from water bearing intervals. This workflow involves ultrasonic and Electric Borehole Images in combination with Sonic Scanner for natural fractures detection, localization and characterization, integrating Dielectric recording and processing for petrophysical evaluation, then Formation Testing was carried out for fluid identification and sampling. The use of borehole electric and sonic imager coupled with advanced sonic acquisition helped not only to identify the natural fractures depths, but also the nature of these fractures. This integration was used for selecting the sampling station.