Fluid flow invasion is governed by the characteristics of the pore structure in the porous media. The flow is restricted due to the increase of net stresses near borehole after drilling. Understanding the fluid flow at the pore scale becomes an important aspect of successful operations such as matrix acidizing or fluid formation treatment. The aim of this work is to demonstrate the ability of using Lattice-Boltzmann formulations to solve the complex fluid flow system in the porous medium as compared to a Darcy type of flow analysis using geomechanical approaches. The simulations were performed at the same physical scale. The reduction of pore size at the vicinity of the borehole is approximated using different geometrical domains to mimic the fluid invasion process as a result of change in net stresses.
Tavassoli, Shayan (The University of Texas at Austin) | Krishnamurthy, Prasanna (The University of Texas at Austin) | Beckham, Emily (The University of Texas at Austin) | Meckel, Tip (The University of Texas at Austin) | Sepehrnoori, Kamy (The University of Texas at Austin)
Storage of large amounts of CO2 within deep underground aquifers has great potential for long-term mitigation of climate change. The U.S. Gulf Coast is an attractive target for CO2 storage because of the favorable formation properties for injection and containment of CO2. Deltaic formations are one of the primary targeted depositional environments in the Gulf Coast. This paper investigates CO2 storage in deltaic saline aquifers through a combination of geological modeling and flow simulation.
The geological model in our study is developed based on a laboratory-scale 3D flume experiment replicating the formation of a delta structure and populated with geologic properties according to Miocene Gulf of Mexico natural analogues. We used invasion percolation simulations to understand the gravity- driven flow and the relationship between architecture, stratigraphy, and fluid migration pathways. The results were used to develop an upscaled model for compositional simulation with the key features of the original geological model and to determine injection schemes that maximize the injection capacity and minimize the amount of mobile CO2 in the formation. In order to achieve this, we used compositional reservoir simulations to study the pressure-driven flow and phase behavior.
The results of invasion percolation simulations were used to identify the key stratigraphic units affecting CO2 migration. The realistic geometries and high resolution of the model facilitate the transfer of results from synthetic to subsurface data. The results allow for the analysis of deltaic depositional environments, important stratigraphic surfaces, and their impact on CO2 storage. The reservoir simulation model and phase behavior were validated against available field and lab data. The results of reservoir simulations were used to investigate the effects of main mechanisms, such as gas trapping and solubilization, on storage capacity. We compared our simulation results on the basis of invasion percolation (gravity driven) and reservoir simulation (pressure driven). The comparison is helpful to understand the strengths and weaknesses of each approach and determine best practices to evaluate CO2 migration within similar formations.
The unique and extremely well characterized deltaic model allows for unprecedented representation of the depositional aquifer architecture. This research combines geologic modeling, flow simulation, and application for CO2 storage. The integrated conclusions will constrain predictions of actual subsurface flow performance and CO2 storage capacity in deltaic systems, while identifying potential risks and primary stratigraphic migration pathways. This research gives insights on prediction of CO2 storage performance and characterization of prospective saline aquifers.
We demonstrate how key geological uncertainties in a giant onshore carbonate reservoir in the Middle East, most notably fracture permeability and saturation distribution, impact the quality of the history match and change the performance forecasts of a planned Miscible Water Alternating Gas (MWAG) injection process. To achieve this, we used a history matching and multi-objective optimisation (MOO) workflow that was tightly integrated with an innovative reservoir modelling workflow that paid particular attention to the fracture and saturation modelling.
Different geological models for the reservoir were designed by integrating static and dynamic data. These data indicated the need to consider fault-related fractures and to update the saturation distribution in the reservoir model. The effective medium theory was therefore used to estimate effective permeability in order to capture the presence of low-intensity fault-controlled fractures in the reservoir. The integration of Special Core Analysis (SCAL) and log-derived J-functions allowed us to build alternative saturation models that honoured well data with great accuracy. The resulting history matched models therefore accounted for the key geological uncertainties present in the reservoir. Afterwards, MOO was applied for each history matched model to identify well controls that optimally balanced the need to maximise the time on the plateau rate while adhering to the field's gas production constraints.
Our results clearly show that including low-intensity fault-controlled fractures in the reservoir model improved the quality of the history match for the gas oil ratio (GOR), bottom hole pressure (BHP) and water cut. This is especially true for wells located near faults, which were difficult to match in the past. Moreover, our results further show that the updated saturation model improved the quality of the history match for the water cut, particularly for wells located in the transition zone. These different history matched models yielded different production forecasts, where the time at which the reservoir can be produced at the plateau rate varied by up to ten years.
Applying MOO for each history matched model then allowed us to identify well controls for the MWAG injection that could extend the time at which the reservoir would be produced at the plateau rate for up to nine years and the risk of losing production plateau down to two years, while always adhering to the current field operational constraints.
We demonstrate how the integration of MOO with an innovative workflow for fracture and saturation modelling impacts the prediction of a planned MWAG injection in a giant onshore carbonate reservoir. Our work clearly illustrates the potential of integrating MOO with new reservoir characterisation methods to improve the quantification of uncertainties in reservoir performance predictions in carbonate reservoirs.
Field presented here is located in offshore Abu Dhabi, consisting of multi-stacked reservoirs with different fluid and reservoir properties. In this paper, field development plan of one of reservoir has been presented which was initially planned to be developed with pattern water injection by more than 50 horizontal wells penetrating all the ten oil bearing layers from 9 well head towers. Reservoir consists of under-saturated oil with low gas-oil ratio and low bubble point. Initial 2 years of production was considered as Early Production Scheme (EPS period), during which significant amount of early production data consisting of downhole pressure measurement, time-lapse MDT, vertical interference data, PLT have been collected. Based on EPS data simulation model has been updated. Simulation fits well with the observed pressure gauge and time-lapse MDT data. Updated model gives good prediction for a year of blind test data (including saturation, MDT and porosity) collected from different wells several kilometers away from current development area reflecting a high level of confidence in areal and vertical connectivity representation. Considering other reservoir uncertainties different Development plans have been screened using updated model in order to improve recovery factor and economics. Based on development plan screening study, optimized development option has been chosen for Full Field Development.
This challenging reservoir characterization case study is defined by the interaction between two reservoirs with different production mechanisms: a fractured basement reservoir and an overlying sandstone reservoir. The existing static geologic concept has been significantly enhanced by integrating pressure data from a unique three-year shut-in period to aid modeling of fractured reservoir connectivity. Previously, the seismic dataset was predominantly used to model the fault and fracture network and guide well planning. In the current approach, the full field data set, including all drilling parameters and new reservoir surveillance data were integrated to address uncertainty in the connected hydrocarbon volume and the relative importance of each production mechanism. The result is a reservoir management tool with which to test re-development concepts and effectively manage pressure decline and increasing gas/oil ratio (GOR) and water production.
To achieve a fully integrated history matched model, the first step was to make a thorough review of the existing detailed seismic interpretation, vintage production logging tool runs (PLT's), wireline logs (including borehole image logs (BHI)) and drilling data to find a causal link between hydraulically conductive fractures and well production behavior. In parallel, a material balance exercise was run to incorporate the new pressure data acquired during the field's shut-in period. The results of the material balance analysis were combined with seismic and well data to define the distribution of connected fractures across the field. Additionally, the material balance analysis was used to determine the connected hydrocarbon volume, the distribution of initial oil in-place and the relative hydrocarbon contribution from each production mechanism.
The field is covered by multi-azimuth 3D seismic and 43 vertical to highly deviated development wells, providing significant static and dynamic data for characterizing the distribution of connected fractures. Despite this high quality, diverse and field-wide dataset, prior modeling iterations struggled to sufficiently describe the production behavior seen at the well level. This has resulted in a major challenge to predicting the production behavior of new development wells and planning for reservoir management challenges. Capturing the complex interaction between production variables (including lithology, matrix versus fracture network, geomechanical stresses, reservoir damage and pressure depletion) at a field level instead of at an individual well level resulted in a unified static and dynamic model that reconciles all scales of observation.
This oilfield represents a unique reservoir characterization opportunity. The result is a key example of how iterative, integrated geological and engineering driven reservoir modeling can be used to inform the development in a complex, mature field. This case study provides an excellent analogue for the reservoir characterization of other fractured Basement fields and/or Basement-cover reservoir couplet fields in the early to late phases of their development.
Reservoir simulation optimization under uncertainty typically invokes a sense of anxiety mainly because of a lack of a systematic criterion to choose between different development scenarios under uncertainty, how to go about doing well placement and optimizing well controls in the face of a large uncertainty ensemble of static realisations, and most of all the large number of simulation runs that potentially needs to be conducted. This is exacerbated when the models are large and require many hours to run. Moreover, even with the prevalence of distributed and parallel computing clusters, there is still a limited amount of computing resources available when spread out over the number of reservoir engineers within a company. Time and budget constraints also contribute to complicating this process. Furthermore, with the requirement of an inordinately large number of simulation runs comes the dilemma as to which optimizer to choose that would help speed up the process.
This paper first starts off with a brief background into historical attempts at tackling this problem by delving into the literature. Then it discusses a rigorous criterion for optimization under uncertainty viz. stochastic dominance, hitherto little known or used in the industry. A commonly used greenfield case study which is an ensemble set of uncertainty realisations is then introduced, which the rest of the paper will be based on. The ensemble is a pre-generated set of fifty realisations designed specifically for this problem. Two challenging areas will then be addressed viz. well placement optmisation under uncertainty, and well controls optimization under uncertainty.
Finally, a comparison between the simplex, proxy response surface, differential evolution and particle swarm optimization methods is made in the optimization of well controls. Hence the paper aims to give a complete picture on how to go about reservoir simulation optimization under uncertainty, with a drastically reduced amount of computational runs that needs to be conducted. Practical and sensible formulation of the optimization problemcan go a long way to making this process more understandable and easier to implement.
Numerous carbonate reservoir discoveries were made in Indonesia (
The process involves multiple cycles—from formation evaluation (e.g., geomechanics analysis, design of an effective fracturing method, and production forecasting) through the economic impact to the operator. During the early phase of this integrated study, the uncertainties of all static and dynamic parameters (i.e., geological complexity, rock physics, and stress profile) were considered for fracturing design. Production performances from multiple fracturing stimulation scenarios were then modeled and compared to select the plan that optimizes production for the Berai Formation.
Results demonstrated an effective multidiscipline approach toward a comprehensive strategy to meet the ultimate objective in optimizing production. This project leveraged formation evaluation and fracturing design to deliver integrated solutions from exploration to accurate production forecast. The well stimulations were performed by carefully selecting fluid characteristics based on geological-petrophysical properties, pressure, and stress profiles within the area. Results yielded excellent production gains—for the best case, up to 50% with an average of 40% in comparison with initial production by using an acid that provides optimum fracture geometry and permeability.
This opportunity demonstrated the importance of understanding formation behavior and the parameters that aid the selection of an appropriate fracturing design for a low porosity/permeability carbonate reservoir.
Reservoir simulations constitute a cornerstone to predict the flow of fluids through porous media. Various numerical models called simulators are developed to simulate the performance of hydrocarbon reservoirs. These models are used in field development since production forecasts are necessary to make investment decisions. Nowadays, numerical simulators are widely used by reservoir engineers.
In recent times, machine learning applications have garnered the interest of the oil and gas industry due their unorthodox approach to creating complex models. The more historical data that can be provided in the training phase of the computation, the more accurate the predictions utilizing less time and computational power required by the alternative. The alternative, reservoir simulation applications, require more robust hardware for processing large amounts of data in sophisticated ways. This will require several iterations and may require long computation to validate the model. Running time for a simulation is dependent on software, which can be dependent on hardware. This creates a matrix of time and resources needed to complete simple to complicated simulations.
A few studies have proposed the use of the Lanczos decomposition method in reservoir simulation studies. The attractiveness of this method appears to be the avoidance of time stepping in simulation and allows the computation of reservoir pressures at any given time directly.
In this study, two new simulators were developed using Lanczos Decomposition Method (LDM) and Conventional Implicit Time-Stepping Method (ITSM). The study focuses on 2-D flow for slightly compressible fluid of constant viscosity with multiple wells. Derivation of the model equations was performed using the continuity equation for both methods through the use of MATLAB. The simulators were written using the MATLAB programming language. The simulators developed in this study are capable of assigning uniform and non-uniform gridblock distribution; porosity and permeability distributions, as well as developing various production and injection scenarios for single or multiple wells depending on different areas of application. Validity and accuracy of the 2D flow simulator were examined by comparing simulation results with that obtained from the commercial software called ECRIN. The results of the simulator were almost identical with the results obtained from the commercial software. During the model runs, the CPU time of the two simulators were compared. A special case was also studied for a single well with variable rate history using both ITSM and LDM written with FORTRAN.
To date, in petroleum engineering literature, there is no work published that compares the performances (in terms of computational aspects as well as CPU times) of the Lanczos method and the conventional implicit-time stepping method.
Hjeij, Dawood (Division of Sustainable Development, College of Science and Engineering, Hamad Bin Khalifa University) | Abushaikha, Ahmad (Division of Sustainable Development, College of Science and Engineering, Hamad Bin Khalifa University)
Most commercially available simulators use the trivial two-point flux approximation (TPFA) method for flux computation. However, the TPFA only gives consistent solutions when used for K-orthogonal grids. In general, multi-point flux approximation (MPFA) methods perform better under both heterogeneous and anisotropic conditions. The mimetic finite difference (MFD) method is designed to preserve properties on unstructured polyhedral grids, and its development for simulating full tensor permeabilities is also crucial step. This paper compares the performance, accuracy, and efficiency of these schemes for simulating complex synthetic and realistic hydrocarbon reservoirs.
Raghunathan, Murali (ADNOC - Al Dhafra Petroleum Company) | Alkhatib, Mohamad (ADNOC - Al Dhafra Petroleum Company) | Al Ali, Abdulla Ali (ADNOC - Al Dhafra Petroleum Company) | Mukhtar, Muhammad (ADNOC - Al Dhafra Petroleum Company) | Doucette, Neil (ADNOC - Al Dhafra Petroleum Company)
A novel workflow was developed to select an optimal field development plan (FDP) which accounts for a number of associated uncertainties for an oil Greenfield concession that has a limited number of wells, production data and information. The FDP was revisited and updated to address the additional data acquired during the field delineation phase. The study in Ref-1 demonstrates the comprehensive uncertainty analysis performed and the resulting optimized FDP. The FDP was developed to minimize the economic risk and uncertainty. Further field delineation activities have revealed a north and south extensions with an increase in hydrocarbon accumulation by 115%. A reservoir dynamic model was updated because of the increase in HC and input data from 17 wells. A workflow has been created with a suitable development option to consider the recently appraised areas, which are: - Updated saturation height functions (SHFs) which improve the match between newly drilled wells and water saturations logs - Updated reservoir models which were based on well tests and new analytical interpretations - History matching well test data with new acquisition data - Optimized field development options, that cover additional areas - Inputs to reservoir surveillance plan Be implementing following an extensive analysis the most robust development concept was selected and will now in the field.