Fluid Dynamics

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 L₂ error follows the opposite trend to that of mesh size, which means that more refinement level gives less L₂ error. For the upscaling of absolute permeability, the relative error can be decreased to 2.27%, which confirms that the proposed method is capable of calculating the absolute permeability with higher refinement levels. The fitting degree of the simulated water phase relative permeability to the original one is better than that of oil phase. The average relative error of water pahse relative permeability upscaling can decrease to less than 5.0%. It is found that the results will get worse when the model includes less elements. Especially at low water saturation, there exists some fluctuations for relative permeability curves and it may be due to the unstable state of the waterflood front with less elements involved.

A giant brownfield re-development project with long horizontal wells was initiated to arrest production decline mainly caused by a lack of pressure support and free gas influx from the large gas cap.

Key value drivers for the project are developing an understanding of the layers with regards to gas breakthrough, and achieving capital efficiency through low-cost well delivery, better planning and technology applications.

Firstly, the field has been segmented based on the analysis of multiple factors influencing the free gas production. It considers geological aspects such as the study of depositional environment and diagenesis, structural elements such as high permeability streaks and fractures, dynamic behaviors such as the water injection efficiency, gas cap expansion or coning.

Secondly, numerical simulations were then run in order to rank the sectors based on the expected model performance, compare them with real data categorization, and test the effect of the new proposed development schemes such as water injection at gas-oil contact and long horizontal wells equipped with downhole control valves.

It was found that each sector has a specific production mechanism and appropriate developments were recommended and then tested in the simulation. For instance, high permeability streaks play a significant role on the development of some sectors instigating a big difference of maturity between sub-layers, early water or gas breakthrough. Also, the inefficiency of water injection is one of the biggest issues of the field. Most of the water injectors are located too far from the oil producers, and have a low injectivity due to the often degraded facies in the aquifer because of diagenesis. This leads to a lack of pressure support that is counterbalanced by the gas injection, ending up with a lot of high GOR wells and a bad sweep from the top of the structure as the gas tends to by-pass the oil.

Simulation work showed that several remaining zones are safe for immediate development and should be prioritized for development in the near future. On the other hand, some of the mature layers prone to gas and water breakthrough need a boost for development, such as water injection at gas-oil-contact, artificial lift, low pressure system, GOR relaxation. Tight and undeveloped reservoirs are improved by implementing long horizontal drains.

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.

The high CO₂ content of Brazil’s pre-salt fields, which may reach values from 20% to 44% molar, presents both a challenge as well as an opportunity. CO₂ stripped from the produced gas cannot be released into the atmosphere due to environmental restrictions. Therefore, the whole amount of CO₂ produced should be continuously reinjected into the reservoir. This work investigates the effect of CO₂ content on the low salinity water alternating CO₂ injection technique (CO₂LSWAG) using a commercial compositional reservoir simulator. In these field-scale simulations, CO₂ is stripped from the produced gas and reinjected into the reservoir. Primary oil recovery methods such as CO₂ flooding and LSW flooding are also simulated. Chemical reactions between CO₂ and the minerals present in the reservoir are modeled. Wettability change is assumed to be the main mechanism for improved oil recovery due to low salinity water injection. Compositional simulations of CO₂ injection usually assume a constant injected gas rate. In this case, CO₂ is supposed to come from an external source. In many petroleum reservoirs this assumption is true. Three factors are assessed in the present work. The first one is the natural reservoir pressure, which is the main driving force in primary production. The second factor is the amount of CO₂ available for injection. The third one is the wettability change promoted by the reaction involving CO₂. It is shown that in primary production, higher CO₂ content leads to quicker depletion of the natural energy of the reservoir, leading to lower oil recovery. Nevertheless, higher CO₂ content also means that more gas is available for reinjection, potentially leading to increased oil production. Finally, as CO₂ reacts with minerals it promotes a change in wettability from an oil-wet to a water-wet state. It is shown that the CO₂ content is an important variable to be assessed in a high CO₂ content reservoir. Optimal injection practices must take these three aspects into consideration.

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 paper describes the results of an integrated reservoir study aimed at producing hydrocarbons through a sustainable development from a green High Temperature (HT) giant CO2-rich gas field in the Australian offshore. The development concept addressed the complex challenge of exploiting resources while minimizing the carbon impact.

In order to characterize the reservoir in the most detailed way and to describe the fluids behaviour, a 1.8 million active cells compositional model has been built. An analytical aquifer has been coupled in order to represent the boundary conditions of the area.

The faults system, interpreted on seismic data by geophysicists, has been included in the simulation model. The selected development plan includes the re-injection of the produced CO2 into the aquifer of the reservoir itself. The supercritical CO2-brine relative permeability curves at reservoir conditions have been provided by Eni laboratories, where the experiments were performed.

Therefore, a detailed model has been built with the purpose of: –

Defining producing well and CO2 injector well locations, numbers and phasing to evaluate expected CO2 injectivity and CO2 breakthrough issues;

For oil and gas companies, accelerating the time to first hydrocarbon is a strategic objective. Special core analysis programs for tight gas reservoirs may take many months because of the long equilibration times involved in the tests. This represents a bottleneck for achieving the goal of reducing the time-to- market. Both log interpretation and reservoir modelling activities are impacted by the long SCAL durations. In order to face the challenge, a suite of fast methods have been developed. They are fast because they operate under non-equilibrium conditions. The methods give the m&n parameters for electric log interpretation, the endpoint gas relative permeability and the relationship linking initial gas saturation, trapped gas saturation and endpoint water relative permeability in a couple of days.

An algebraic dynamic multilevel (ADM) method for multiphase flow in heterogeneous fractured porous media using the projection-based embedded discrete fracture model (pEDFM) is presented. The fine-scale discrete system is obtained independently for matrix and each lower-dimensional fracture. On the fine-scale high resolution computational grids, an independent dynamic multilevel gird (i.e., ADM grid) is imposed. The fully implicit discrete system is mapped completely algebraically to this ADM grid resolution using sequences of restriction and prolongation operators. Multilevel multiscale basis functions are locally computed and employed to honor the heterogeneity contrasts of the fractured domain by interpolating the solution accurately. These basis functions are computed only at the beginning of the simulation to increase the computational efficiency. Once the ADM system is solved for all unknowns (i.e., pressure and saturation), the solution at ADM resolution is prolonged back to fine-scale resolution in order to obtain an approximated fine-scale solution. This dynamic multilevel system employs the fine-scale grid cells only at the sharp gradient of the solution (e.g., at the moving front). With two fractured test-cases (homogeneous and heterogeneous), the performance of ADM is assessed by comparing it to fine-scale results as reference solution. It will be shown that ADM is able to reduce the computational costs and provide efficiency while maintaining the desired accuracy.

An innovative multi-deterministic scenario workflow was applied to one of the giant and complex carbonate reservoirs in the Middle East. The application of this workflow had the objective to quantify how geological uncertainties and different modelling decisions impact the stock tank oil-initially-in-place (STOIIP) estimates and flow behaviour in this reservoir. In particular, we focused on the uncertainties related to the presence of fractures, reservoir rock typing, and modelling the initial hydrocarbon distribution.

Based on the available static and dynamic data we considered two key scenarios, the absence of fractures and the presence of sparse, fault-controlled fractures. In the first scenario, we investigated how different reservoir rock typing methods impact permeability distributions. We further quantified changes in hydrocarbon distribution and analysed how a novel approach that combines capillary pressure and log-derived J-function affects the saturation models. In the second scenario, we used the effective medium theory to calculate permeability multipliers for the regions where fractures are expected. This enabled us to effectively represent fractures in a single-porosity reservoir model. The representativeness of the different models was analysed through blind tests using static data as well as history matching using dynamic data.

The most significant findings of our work are that subtle changes in modelling decisions and reservoir rock typing have major consequences for the saturation model, leading to up to 20% change in STOIIP estimates. Such uncertainties must be carried forward in future reservoir management decisions and when estimating reserves. The blind tests showed that a saturation model based on the combination of core- and log-derived J-functions gave the most robust STOIIP estimates. These particular saturation models further led to a much-improved history match, especially for wells located in the transition zone of the reservoir. The best history matches were obtained once sparse, fault-controlled fractures were included in the reservoir model using effective medium theory. The presence of fractures specifically improved the history matching quality for wells located close to the faults; these wells were very difficult to match in the past.

Our work clearly demonstrates that a multi-deterministic scenario workflow is key to explore the appropriate range of geological uncertainties, and that, equally important, the impact of different modelling decisions must be accounted for when quantifying uncertainty during reservoir modelling. This is particularly applicable to giant carbonate reservoirs where relatively minor changes in the workflow and data interpretation can have major consequences on STOIIP estimates, dynamic behaviours, and reserve estimates. Multi-stochastic modelling workflows which anchor the reservoir to a single base case are not capable of achieving this.