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Collaborating Authors
Results
Reservoir Simulation of Steam Fracturing in Early-Cycle Cyclic Steam Stimulation
Cokar, Marya (Department of Chemical and Petroleum Engineering, Schulich School of Engineering, University of Calgary) | Kallos, Michael S. (Department of Chemical and Petroleum Engineering, Schulich School of Engineering, University of Calgary) | Gates, Ian D. (Department of Chemical and Petroleum Engineering, Schulich School of Engineering, University of Calgary)
Summary In cyclic steam stimulation (CSS), steam is injected above the fracture pressure into the oil-sands reservoir. In early cycles, the injected steam fractures the reservoir, creating a relatively thin dilated zone that allows rapid distribution of heat within the reservoir without excessive displacement of oil from the neighborhood of the wellbore. Numerical reservoir-simulation models of CSS that deal with the fracturing process have difficulty simultaneously capturing flowing bottomhole-pressure (BHP) behavior and steam injection rate. In this research, coupled reservoir-simulation (flow and heat transfer) and geomechanics models are investigated to model dynamic fracturing during the first cycle of CSS in an oil-sands reservoir. In Alberta, Canada, in terms of volumetric production rate, CSS is the largest thermal recovery technology for bitumen production, with production rates equal to approximately 1.3 million B/D in 2008. The average recovery factor from CSS is between 25 and 28% at the economic end of the process. This implies that the majority of bitumen remains in the ground. Because the mobility of the bitumen depends strongly on temperature, the performance of CSS is intimately linked to steam conformance in the reservoir, which is largely established during steam fracturing of the reservoir in the early cycles of the process. Thus, a fundamental understanding of the flow and geomechanical aspects of early-cycle CSS is critical. A detailed thermal reservoir-simulation model, including dilation and dynamic fracturing, was developed, with the use of a commercially available thermal reservoir simulator, to understand their effects on BHP and injection rate. The results demonstrate that geomechanics must be included to accurately model CSS. The results also suggest that the reservoir dilates during steam injection as the result of increases in reservoir temperature, which lead to thermal dilation and higher pore pressure.
- North America > United States (1.00)
- North America > Canada > Alberta (1.00)
- Geology > Petroleum Play Type > Unconventional Play > Heavy Oil Play (1.00)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- North America > Canada > British Columbia > Peace River Field (0.99)
- North America > Canada > Alberta > Western Canada Sedimentary Basin > Alberta Basin > Primrose Field > Clearwater Formation (0.99)
- North America > Canada > Alberta > Western Canada Sedimentary Basin > Alberta Basin > Clearwater Formation (0.99)
- (3 more...)
History Matching Saturation and Temperature Fronts with Adjustments of Petro-Physical Properties; SAGD Case Study
Mirzabozorg, Arash (University of Calgary) | Nghiem, Long (Computer Modelling Group Ltd.) | Chen, Zhangxing (University of Calgary) | Yang, Chaodong (Computer Modelling Group Ltd) | Hajizadeh, Yasin (Computer Modelling Group Ltd)
Abstract History matching of reservoir flow models based only on production data may not reveal deficiencies that affect future predictions. Incorporating saturation and temperature profile data that come from 4D seismic surveys in the history matching process can reduce the uncertainty of reservoir models for the prediction stage. We constructed a field reservoir model from which production history, saturation and temperature profile history were obtained. We started the history matching process with a base reservoir model, the petro-physical properties of which were substantially different than those of the field reservoir model. We propose a new methodology for matching the fluid and temperature profiles by adjusting reservoir petro-physical properties. In this methodology, some grid blocks in a reservoir model were selected judiciously to capture the overall saturation and temperature distribution profiles. In addition to well production data, we included the saturation and temperature profiles at these grid blocks as extra objective functions during the history matching process. The DECE optimization is used to reduce the objective function. We applied this method in a Steam Assisted Gravity Drainage (SAGD) process and matched the saturation and temperature profiles with an average error of less than 2%.
- Geophysics > Seismic Surveying (1.00)
- Geophysics > Time-Lapse Surveying > Time-Lapse Seismic Surveying (0.72)
Evaluation of the Effect of Asphaltene Deposition in the Reservoir for the Development of the Magwa Marrat Reservoir
Al-Qattan, A.. (KOC) | Blunt, M. J. (Imperial College) | Gharbi, O.. (Imperial College) | Badamchizadeh, A.. (CMG) | Al-Kanderi, J. M. (KOC) | Al-Jadi, M.. (KOC) | Dashti, H. H. (KOC) | Chimmalgi, V.. (KOC) | Bond, D. J. (KOC) | Skoreyko, F.. (CMG)
Abstract The Magwa Marrat reservoir was discovered in the mid-1980s and has been produced to date under primary depletion. Reservoir pressure has declined and is approaching the asphaltene onset pressure (AOP). A water flood is being planned and a decision needs to be taken as to the appropriate reservoir operating pressure. In particular the merits of operating the reservoir at pressures above and below the AOP need to be assessed. Some of the issues related to this decision relate to the effects of asphaltene deposition in the reservoir. Two effects have been evaluated. Firstly the effect of in-situ deposition of asphaltene on wettability and the influence that this may have on water-flood recovery has been investigated using pore scale network modes. Models were constructed and calibrated to available high pressure mercury capillary pressure data and to relative permeability data from reservoir condition core floods. The changes to relative permeability characteristics that would result from the reservoir becoming substantially more oil-wet have been evaluated. Based on this there seems to be a very limited scope for poorer water flood performance at pressures below AOP. Secondly the scope for impaired well performance has been evaluated. This has been done using a field trial where a well was produced at pressures above and substantially below AOP and pressure transient data were used to estimate near wellbore damage "skin". Also compositional simulation has been used to estimate near wellbore deposition effects. This has involved developing an equation of state model and identifying, using computer assisted history matching, a range of parameters that could be consistent with core flood experiments of asphaltene deposition. Results of simulation using these parameters are compared with field observation and used to predict the range of possible future well productivity decline. Overall this work allows an evaluation of the preferred operating pressure, which can drop below the AOP, resulting in lower operating costs and higher final recovery without substantial impairment to either water-flood efficiency or well productivity.
- Asia > Middle East > Kuwait (0.69)
- North America > United States > Texas (0.68)
- Energy > Oil & Gas > Upstream (1.00)
- Materials > Chemicals > Commodity Chemicals > Petrochemicals (0.46)
- Asia > Middle East > Kuwait > Ahmadi Governorate > Arabian Basin > Widyan Basin > Greater Burgan Field > Wara Formation (0.99)
- Asia > Middle East > Kuwait > Ahmadi Governorate > Arabian Basin > Widyan Basin > Greater Burgan Field > Ratawi Formation (0.99)
- Asia > Middle East > Kuwait > Ahmadi Governorate > Arabian Basin > Widyan Basin > Greater Burgan Field > Mauddud Formation (0.99)
- (3 more...)
Abstract The need to develop new tools that allow reservoir engineers to optimize reservoir performance is becoming more demanding by the day. One of the most challenging and influential problems facing reservoir engineers is well placement optimization. The North Kuwait field (NKF) consists of six fields containing four naturally fractured carbonate formations. The reservoirs are composed of relatively tight limestone and dolomite embedded with anhydrate and shale. The fields are divided into isolated compartments based on fault zones and supported by a combination of different fluid compositions, initial pressures, and estimated free-water levels. Due to natural complexity, tightness, and high drilling costs of wells in the NKF, it is very important to identify the sweet spots and the optimum well locations. This paper presents two intelligent methods that use dynamic numerical simulation model results and static reservoir properties to identify zones with a high-production potential: reservoir opportunity index (ROI) and simulation opportunity index (SOI). The Petrel* E&P software platform was chosen as the integrated platform to implement the workflow. The fit-for-purpose time dependent 2D maps generated by the Petrel platform facilitated the decision-making process used for locating new wells in the dominant flow system and provided immense support for field-development plans. The difference between the two methods is insignificant because of reservoir tightness, limited interference, and natural uncertainty on compartmentalization. At this stage, pressure is not a key parameter. As a result, unlike brown fields, less weight was given to simulated pressure, and SOI was used to select the well locations. The results of this study show that implementing these workflows and obtaining the resulting maps significantly improve the selection process to identify the most productive areas and layers in a field. Also, the optimum numbers of wells using this method obtained in less time and with fewer resources are compared with results using traditional industry approaches.
- Asia > Middle East > Kuwait > Jahra Governorate > Arabian Basin > Widyan Basin > North Kuwait Jurassic (NKJ) Fields > Marrat Formation > Upper Marrat Formation (0.98)
- Asia > Middle East > Kuwait > Jahra Governorate > Arabian Basin > Widyan Basin > North Kuwait Jurassic (NKJ) Fields > Marrat Formation > Sargelu Formation (0.98)
Abstract Resistivity measurements play a key role in hydrocarbon in place calculations for oil and gas reservoirs. They are a direct indicator of fluid saturation and connected pore space available in the formation. Carbonate rocks, which host around half of the world's hydrocarbons, exhibit a wide range of porosities with scales spanning from nanometres to centimetres. The often significant amount of microporosity displayed by Carbonate rocks emphasizes the necessity of an adequate characterization of their micro-features and their contribution to hydrocarbon in place. In this paper we examine upscaling methods to probe formation factor of a fully saturated carbonate sample using an X-ray CT based numerical approach and compare to experimental measurements. Three-dimensional high-resolution X-ray CT enables the numerical calculation of petrophysical properties of interest at the pore scale with resolutions down to a few microns per voxel. For more complex and heterogeneous samples however, a direct calculation of petrophysical properties is not feasible, since the required resolution and a sufficient field of view cannot be obtained simultaneously. Thus an integration of measurements at different scale is required. In this study a carbonate sample of 38mm in diameter is first scanned using the X-ray CT method with a resolution of 26 ยตm. After accompanying experimental measurements on the full plug, four 5mm plugs were drilled vertically from this sample and X-ray CT images of these plugs acquired at resolutions down to 2.74 ยตm. We calculate the porosity of the sample (macro- and micro-porosities) using the phase separation methods and then predict the formation factor of the sample at several scales using a Laplace solver. The formation factor is calculated by using a general value of m=2 as cementation factor for intermediate porosity voxels. We compare to experimental measurements of formation factor and porosity both at the small plug and full plug scale and find good agreement. To assess the degree of uncertainty of the numerical estimate, we probe the extent of heterogeneity by investigating the size of a representative elementary volume (REV) for formation factor. We find that for the considered heterogeneous carbonate sample, formation factor varies considerably over intervals less than a centimetre. Our results show that this variation could be explained by different cementation exponents applied at the micro-voxel scale, with the exemption of one plug, for which the cementation exponent would have to be unreasonably low. These cementation factors are derived by direct comparison between numerical simulation and experiment. We conclude that for one plug an error in experimental measurement might have occurred. The numerical approach presented here therefore aids in quality control. Excluding this plug in the upscaling procedure improves the agreement with the experimental result for the whole core while still underestimating formation factor. Allowing for a constant m=2 in the simulation at the small scale and using directly the resulting relationship between porosity and formation factor in the upscaling process leads to an overestimation of formation factor.
- North America > United States > Texas (0.46)
- Asia > Middle East > Saudi Arabia (0.28)
- North America > United States > Texas > Permian Basin > Yeso Formation (0.99)
- North America > United States > Texas > Permian Basin > Yates Formation (0.99)
- North America > United States > Texas > Permian Basin > Wolfcamp Formation (0.99)
- (26 more...)
Delivering Optimal Brownfield Development Strategies: Use of Multi-Constrained Optimisation Combined with Fast Semi-Analytical and Numerical Simulators
Alvi, A.. (Schlumberger) | Tilke, P.. (Schlumberger) | Bogush, A.. (Schlumberger) | Kolupaev, A.. (Schlumberger) | Jilani, S. Z. (Schlumberger) | Banerjee, R.. (Schlumberger) | Bolanos, N.. (Schlumberger) | Wu, J.. (Schlumberger)
Abstract Assessing the waterflood, monitoring the fluids front, and enhancing sweep with the uncertainty of multiple geological realisations, data quality, and measurement presents an ongoing challenge. Defining sweet spots and optimal candidate well locations in a well-developed large field presents an additional challenge for reservoir management. A case study is presented that highlights the approach to this cycle of time-lapse monitoring, acquisition, analysis and planning in delivery of an optimal field development strategy using multi-constrained optimisation combined with fast semi-analytical and numerical simulators. The multi-constrained optimiser is used in conjunction with different semi-analytical and simulation tools (streamlines, traditional simulators, and new high-powered simulation tools able to manage huge, multi-million-cell-field models) and rapidly predicts optimal well placement locations with inclusion of anti-collision in the presence of the reservoir uncertainties. The case study evaluates proposed field development strategies using the automated multivariable optimisation of well locations, trajectories, completion locations, and flow rates in the presence of existing wells and production history, geological parameters and reservoir engineering constraints, subsurface uncertainty, capex and opex costs, risk tolerance, and drilling sequence. This optimisation is fast and allows for quick evaluation of multiple strategies to decipher an optimal development plan. Optimisers are a key technology facilitating simulation workflows, since there is no โone-approach-fits-allโ when optimising oilfield development. Driven by different objective functions (net present value (NPV), return on investment (ROI), or production totals) the case study highlights the challenges, the best practices, and the advantages of an integrated approach in developing an optimal development plan for a brownfield.
- Reservoir Description and Dynamics > Reservoir Simulation (1.00)
- Reservoir Description and Dynamics > Improved and Enhanced Recovery > Waterflooding (1.00)
- Management > Asset and Portfolio Management > Field development optimization and planning (1.00)
- Data Science & Engineering Analytics > Information Management and Systems (1.00)
Abstract The North Kuwait Jurassic Gas (NKJG) reservoirs are currently under development by KOC. The fractured carbonate reservoirs contain gas condensate and volatile oil at pressures up to 11,500 psi with 2.9% H2S and 1.5% CO2. Currently around 20 active wells are producing to an Early Production Facility (EPF-50) that falls short of achieving the desired capacity and capability to handle production efficiently. To understand wells and field performance, an integrated system model comprising of wells, flow line and gathering system separator network was created. The setting up a model and its use is an integral subset of WRFM (Wells, Reservoir and Facilities Management) process that is essential for effectively managing the current asset and for further field development. The application of the model is to be an enabler for wider implementation of the WRFM process in KOC and a tool to meet the following objectives: It should closely match the EPF-50 throughput It should identify problem wells It should identify well intervention and data gathering opportunities It should be a tool for training engineers on nodal analysis and WRFM It can be used for back allocation It could be used to benchmark against any existing models The model has shown close approximation with field metered production and is already achieving many of its desired objectives. This paper describes the use of integrated nodal analysis model to generate data gathering and well intervention opportunities not only to operate the facilities efficiently but understand well and reservoir behavior for input to full field development plan.
- Geology > Rock Type > Sedimentary Rock (0.47)
- Geology > Sedimentary Geology > Depositional Environment (0.47)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Well performance, inflow performance (1.00)
- Reservoir Description and Dynamics > Fluid Characterization > Phase behavior and PVT measurements (1.00)
- (4 more...)
Heavy Oil and Tar Mat Characterization Within a Single Oil Column Utilizing Novel Asphaltene Science
Seifert, Douglas J. (Saudi Aramco) | Qureshi, Ahmed (Schlumberger) | Zeybek, Murat (Schlumberger) | Pomerantz, Andrew E. (Schlumberger) | Zuo, Julian Y. (Schlumberger) | Mullins, Oliver C. (Schlumberger)
ABSTRACT A Jurassic oil field in Saudi Arabia is characterized by black oil in the crest, with heavy oil underneath and all underlain by a tar mat at the oil-water contact (OWC). The viscosities in the black oil section of the column are similar throughout the field and are quite manageable from a production standpoint. In contrast, the mobile heavy oil section of the column contains a large, continuous increase in asphaltene content with increasing depth extending to the tar mat. Both the excessive viscosity of the heavy oil and the existence of the tar mat represent major, distinct challenges in oil production. A simple new formalism, the Flory-Huggins-Zuo (FHZ) Equation of State (EoS) incorporating the Yen-Mullins model of asphaltene nanoscience, is shown to account for the asphaltene content variation in the mobile heavy oil section. Detailed analysis of the tar mat shows significant nonmonotonic content of asphaltenes with depth, differing from that of the heavy oil. While the general concept of asphaltene gravitational accumulation to form the tar mat does apply, other complexities preclude simple monotonic behavior. Indeed, within small vertical distances (5 ft) the asphaltene content can decrease by 20% absolute with depth. These complexities likely involve a phase transition when the asphaltene concentration exceeds 35%. Traditional thermodynamic models of heavy oils and asphaltene gradients are known to fail dramatically. Many have ascribed this failure to some sort of chemical variation of asphaltenes with depth; the idea being that if the models fail it must be due to the asphaltenes. Our new simple formalism shows that thermodynamic modeling of heavy oil and asphaltene gradients can be successful. Our simple model demands that the asphaltenes are the same, top to bottom. The analysis of the sulfur chemistry of these asphaltenes by X-ray spectroscopy at the synchrotron at the Argonne National Laboratory shows that there is almost no variation of the sulfur through the hydrocarbon column. Sulfur is one of the most sensitive elements in asphaltenes to demark variation. Likewise, saturates, araomatics, resins and asphaltenes (SARA); measurements also support the application of this new asphaltene formalism. Consequently, the asphaltenes are very similar, and our new FHZ EoS with the Yen-Mullins formalism properly accounts for heavy oil and asphaltene gradients.
- North America > United States (0.94)
- Asia > Middle East > Saudi Arabia (0.48)
- Asia > Middle East > Kuwait > Jahra Governorate > Arabian Basin > Widyan Basin > North Kuwait Jurassic (NKJ) Fields > Marrat Formation > Upper Marrat Formation (0.98)
- Asia > Middle East > Kuwait > Jahra Governorate > Arabian Basin > Widyan Basin > North Kuwait Jurassic (NKJ) Fields > Marrat Formation > Sargelu Formation (0.98)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Oil sand, oil shale, bitumen (1.00)
- Reservoir Description and Dynamics > Reservoir Simulation (1.00)
- Production and Well Operations > Production Chemistry, Metallurgy and Biology > Inhibition and remediation of hydrates, scale, paraffin / wax and asphaltene (1.00)
- Facilities Design, Construction and Operation > Flow Assurance > Precipitates (paraffin, asphaltenes, etc.) (1.00)
Abstract This paper presents a novel implementation for evolutionary algorithms in oil and gas reservoirs history matching problems. The reservoir history is divided into time segments. In each time segment, a penalty function is constructed that quantifies the mismatch between the measurements and the simulated measurements, using only the measurements available up to the current time segment. An evolutionary optimization algorithm is used, in each time segment, to search for the optimal reservoir permeability and porosity parameters. The penalty function varies between segments; yet the optimal reservoir characterization is common among all the constructed penalty functions. A population of the reservoir characterizations evolves among subsequent time segments through minimizing different penalty functions. The advantage of this implementation is two fold. First, the computational cost of the history matching process is significantly reduced. Second, problem constraints can be included in the penalty function to produce more realistic solutions. The proposed concept of dynamic penalty function is applicable to any evolutionary algorithm. In this paper, the implementation is carried out using genetic algorithms. Two case studies are presented in this paper: a synthetic case study and the PUNQ-S3 field case study. A computational cost analysis that demonstrates the computational advantage of the proposed method is presented.
- North America > United States > Texas (0.46)
- Asia > Middle East > UAE (0.28)
- Reservoir Description and Dynamics > Reservoir Simulation > History matching (1.00)
- Reservoir Description and Dynamics > Reservoir Fluid Dynamics > Flow in porous media (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (1.00)
Abstract The purpose of history matching is to achieve geological realizations calibrated to the historical performance of the reservoir. For complex geological structures it is usually intractable to run tens of thousands of full reservoir simulation to trace the most probable geological model. Hence the inadequacy of the history-matching results frequently leads to poor estimation of the true model and high uncertainty in production forecasting. Reduced-order modeling procedures, which have been applied in many application areas including reservoir simulation, represent a promising means for constructing efficient surrogate models. Nonlinear dimensionality reduction techniques allow for encapsulating the high-resolution complex geological description of reservoir into a low-dimensional subspace, which significantly reduces number of unknowns and provides an efficient way to construct a proxy model based on the the reduced-dimension parameters. Polynomial Chaos Expansions (PCE) is a powerful tool to quantify uncertainty in dynamical system when there is probabilistic uncertainty in the system parameters. In reservoir simulation it has been shown to be more accurate and efficient compared to traditional experimental design (ED). PCEs have a significant advantage over other response surfaces as the convergence to the true probability distribution is proved when the order of the PCE is increased. Accordingly PCE proxy can be used as the pseudo-simulator to represent the surface responses of the uncertain variables. When the objective and constraints of a reservoir model is described by multivariate polynomial functions, there are very efficient algorithms to compute the global solutions. We have developed a workflow at which incorporates PCE to find the global minimum of the misfit surface and assess the uncertainty associated with. The accuracy of the PCE proxy increases with the additional trial runs of the reservoir simulator. We conduct a two dimensional synthetic case study of a fluvial channel as well as a real field example to demonstrate the effectiveness of this approach. Kernel Principal Component Analysis (KPCA) is used to parameterize the complex geological structure. The study has revealed useful reservoir information and delivered more reliable production forecasts. PCE-based history match enhances the quality and efficiency of the estimation of the most probable geological model and improve the confidence interval of production forecasts.
- Information Technology > Modeling & Simulation (1.00)
- Information Technology > Data Science > Data Mining (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (0.87)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Optimization (0.68)