Gas-lift is an important artificial lift strategy for increasing the production of hydrocarbons from heavy oil and offshore reservoirs with declining pressure. The optimum design and operation of gas-lift has a considerable impact on the optimum production and economics of the entire field and can only be achieved by considering all related variables in connected reservoirs, gas-lifted wells and facilities. Therefore, it is essential to formulate the gas-lift model as an optimization problem within an integrated modeling environment, where all the time dependent physical and operational constraints of reservoirs, wells and facilities can be collectively taken into account during the time evolutionary modeling of the asset. However, this could pose a computationally challenging problem for most derivative based optimizers, as some of the governing equations and models representing the gas-lifted asset could be very nonlinear, physically or mathematically discontinuo us, and the system may not have a solution under some of the conditions proposed by the optimizer due to physical operational restrictions or infeasibility. To overcome these problems we applied a mesh adaptive direct search (MADS) derivative free optimization technique to optimize the gas-lift problems in an integrated reservoir, wells and production facilities environment. The results proved the suitability of the MADS strategy for optimizing these systems particularly for systems with narrow or discontinuous boundaries of physical operation.

Computing hardware and reservoir simulation technologies continue to evolve in order to meet the ever-increasing requirement for improving computational performance and efficiency in the oil and gas industry. These improvements have enabled the simulation of larger and more complex reservoir models. When working with steam assisted gravity drainage (SAGD) operations, determining the optimal steam injection rates and allocation of steam among various multi-well pads is very important, especially given the high cost of steam generation and the current low oil price environment. As SAGD operations mature, steam chambers start to coalesce and interact with each other, forcing producers to face declining oil rates and increasing steam oil ratios (SOR). Operators must work to reduce injection rates on declining wells to maintain a low SOR and free up capacity for newer, more productive wells. Steam injection and allocation between wells and multiple pads then becomes an exercise of optimizing cost, and improving productivity and net present value (NPV). A case study is performed on a full field SAGD model by optimizing steam delivery aided by Artificial Intelligence (AI) and machine learning enabled algorithms for automated numerical tuning, and dynamic gridding technologies. The model contains 15 pads, 96 well pairs (192 wells), 12.6 million active simulation grid blocks, and represents a typical Athabasca formation geology and fluid properties. The proposed steam delivery optimization considers two main scenarios. The first scenario considers the case in which steam generation capacity is limited, and the optimization process intelligently determines the optimal well and pad level steam injection rates dynamically during the life of the project. The second scenario assumes that steam generation availability is not constrained and the field development plan is optimized based on steam required for maximum recovery from the field as fast as possible. A full field optimized development plan is created for the 15 SAGD pads and 96 well pairs. Following the optimization, an increase in NPV and reduction in SOR is achieved for the entire field due to the efficient utilization of total available steam. The optimization study required several full field SAGD simulations to be completed in a practical time period, demonstrating that workflows such as this can be carried out for full field thermal models. These models can also be used to evaluate production responses due to varying operating strategies in the field. This paper presents the optimization of steam allocation for a full field, multi pad SAGD simulation model. It demonstrates that advances in computing and reservoir simulation technology have enabled the simulation of full field models within a reasonable timeframe, allowing engineers to tackle a new class of problems that were previously impractical.

In this consistently low oil price environment, where infill drilling and new field developments struggle to meet economic metrics, production optimization continues to be a focus and driver for the industry. Currently, waterflooding contributes significantly to global oil production and is one of the main non-thermal techniques that can be applied to increase pool recovery. Although proper reservoir management of assets under Waterflood (WF) is critical to reaching the highest recovery factor (RF) possible, it is difficult to achieve and maintain given the inherent dynamic nature of the production mechanism. Further, to achieve optimum reservoir management while providing the opportunity to leverage alternative enhanced oil recovery (EOR) technology, the existing subsurface and surface infrastructure should be fully optimized. By optimizing the subsurface and surface infrastructure in parallel with achieving optimum reservoir management it will result in higher capital efficiency while improving key economic metrics such as operating expenditure (Opex), reserve replacement ratio, depreciation depletion & amortization (DD&A), and overall earnings. Given the existing challenges that include reservoir conformance problems, lack of reservoir energy, excess fluid production, wellbore and pipeline integrity issues, and infrastructure constraints, how to fully optimize the current infrastructure while achieving optimum reservoir management in parallel is the main question and challenge. Husky Energy’s medium oil reservoir management strategy has been highly successful in reinforcing WF as a sustainable long-term recovery method. This paper will present a practical workflow to tackle the challenges highlighted by using a systematic reservoir and production engineering approach with minimum additional capital expenditure (Capex). First, a robust framework was developed to answer three main questions: "What is happening?", "Why is it happening?" and "How can it be improved?". Then, a comprehensive dynamic surveillance methodology, consisting of both numerical and analytical techniques and a 10-step workflow for optimizing a WF project, is discussed. This is followed by the results achieved by employing this strategy in three of Husky’s WF fields; the Wainwright Sparky, Wildmere Lloydminster and Marsden-Manitou Sparky pools located in the Lloydminster oil block. The positive impact that this reservoir management process has had on all key financial metrics will be discussed. As an example, since the beginning of the optimization initiative the Wainwright and Wildmere pool production has increased 21% and 23% respectively, while the Opex has decreased by 33% and 42%, respectively. Further, since implementing a similar strategy in 2016 at the Marsden-Manitou WF, its production has increased by 30% and its Opex has decreased by more than 18%. Finally, this paper will present a WF protocol check list that has been developed as a guideline for engineers who need to optimize pool performance even in a capital constrained environment.

A novel modeling technology termed " Data Physics " that combines reservoir physics with machine learning is validated against a conventional reservoir simulator for thermal recovery problems, i.e., steam flooding and steam assisted gravity drainage (SAGD). The novelty of the new model is its combination of speed of data integration (less than a week) and runtime (minutes) with long-term predictive accuracy (years or decades). This is due to the unique integration of reservoir physics with fast data-driven methods. For accurate benchmarking, major sources of modeling errors in the finite difference simulations are screened and controlled. Two cases are studied, the SPE4 steamflood model, and a single pad SAGD model. The results demonstrate that the Data Physics model is able to reproduce production profiles and key reservoir physics accurately when numerical errors in simulation are properly accounted for, while also being immune to numerical issues like grid orientation effects that can have significant impact on results of reservoir simulation.

Objectives/Scope Expertise has brought consistency and cost control to drilling and completing steam assisted gravity drainage (SAGD) well pairs. Data shows that the well pad facilities have not achieved the same degree of consistency in scope. Broad differences in specifications, physical design and execution have existed and continue to exist. We intend to show the benefits of technical benchmarking for scope and specification control in the design and execution of SAGD well pads. Methods, Procedures, Process Our approach is to measure constructed and operating well pad designs with 511 category measures which rollup into 42 key design metric categories. We believe that the operating well pads provide evidence of functionality and show good engineering practice. Our interest is in showing the minimum of each kind of equipment, pipe and instrument building block that is actually required to provide a functioning site. Results, Observations, Conclusions Our data comes to us under confidential contract from many of the SAGD producing companies and engineering firms, either for estimating or for comparative analysis of designs with industry normal prior to sanction. We reviewed well pad design elements, physical measurements, sizes and counts and compared them across both industry and design to reveal opportunities for optimization. Our repeated studies of the operational and regulatory compliant well pad scope show that some projects used many times more items than other projects. Large differences in well pad dimensions were often combined with large differences in equipment sizing. Large differences in bulk materials were also observed. Through the identification of design limitations, a number of project teams incorporated significant reductions in on-pad dimensions, pipe and equipment sizes and reductions in counts of instrumentation hardware. This process leads to simplification of the design, allowing for a reduction in capital expenditure (CapEx) and operating costs (OpEx) while ensuring it is still safe and easy to operate. In a low oil-price environment, it is essential to produce well pads comprised of design elements and execution that have measurable success in all areas. Project managers and executives alike need to know that the design teams are utilizing the minimum practical combination of sizing, redundancies and tonnages of materials in execution. It is our belief that good engineering will show what to include but great engineering shows what can be left out. We propose that a minimum, safe, functional scope when combined with good contract strategy will bring the thermal producers supplemental well pad costs that will meet capital requirements in our new low Western Canadian Select crude oil price environment. NOTE: At no point will the contracting entities or producers be identified in this paper. Novel/Additive Information We have seen no other recent work that uses this approach to control scope or specification. We believe the technique is universally applicable to any unconventional development such as shale gas, SAGD or similar.

SAGD process is highly affected by geological conditions such as reservoir heterogeneities caused by the existence of shale layers and/or Clast Breccia, impairment zones and so on. To evaluate SAGD performance with satisfactory precision, detailed geological models which reflect complex sedimentary heterogeneities should be constructed based on data such as well control points, 3D seismic data and Training Image. On the other hand, the relationships between complexity and simulation time conflict with each other. Although detailed geological models are needed, simulation time takes longer. At Hangingstone Demo area, (Demo area), there are 24 well pairs which have been producing bitumen by SAGD process. The objective of this study is to obtain a unique set of parameters, such as permeabilities and extent of heterogeneities, from simultaneous history matching using different geological models (well pairs) from the Demo area. These parameters will be used for forecasting the performance of the nearby analogue Hangingstone Expansion area (HE area) development. At least a couple of well pairs coming from different well pads were chosen as representative models which indicate each typical type of reservoir. These are called Clean Sand rich type, Clast rich, and Sandy-IHS (Inclined Heterolithic Stratification) rich. If different single models are applied to history matching, at least the same number of history matching studies are needed separately. Obtaining an optimum parameter combination will require a significant number of simulation runs. Furthermore, applying complex geological models for simulation requires significant time to finish running. Figure 1 shows the comparison between individual optimization and simultaneous optimization, which is our new workflow. By use of this, not only multiple different models but also different realizations for the same well pair are matched simultaneously by a single optimization without any time loss. Our new workflow is composed of mainly 3 sections. The first one is vertical upscaling and coupling between the modeling package and reservoir simulator. The second one is the simultaneous history matching using multiple different models. The third one is the interpretation of the vertical observation well temperature data and history matching utilizing such data. This new technique along with field observation data will be discussed.

Quantitative appraisal of different operating areas and assessment of uncertainty due to reservoir heterogeneities are crucial elements in optimization of production and development strategies in oil sands operations. Although detailed compositional simulators are available for recovery performance evaluation for SAGD, the simulation process is usually deterministic and computationally demanding, and it not quite practical for real-time decision-making and forecasting. Data mining and machine learning algorithms provide efficient modeling alternatives, particularly when the underlying physical relationships between system variables are highly complex, non-linear, and possibly uncertain. In this study, a comprehensive training set encompassing SAGD field data compiled from numerous publicly-available sources is studied. Exploratory data analysis is carried out to interpret and extract relevant attributes describing characteristics associated with reservoir heterogeneities and operating constraints. Because of their ease of implementation and computational efficiency, knowledge-based techniques including artificial neural networks (ANN) are employed to facilitate SAGD production performance prediction. Predicting (input) variables including porosity, net-to-gross ratio, saturation, gross pay, normalized shale barrier thickness and distance to well pair, and initial production rate are formulated. Measures such as cumulative production over discrete time intervals are considered as prediction (output) variables. Data records that are comprised of both input and output variables are assembled; the network is trained using the data set to identify all significant patterns and relationships that exist between the input and the output variables. The model is subsequently validated using a cross-verification procedure, during which records that have been excluded at the training stage are presented to the model. This paper demonstrates that knowledge-based techniques can be implemented in a practical manner to analyze large amount of competitor data efficiently. The approach can be integrated directly into most existing reservoir management routines. It can also be readily updated when new information has become available. Given that robust reservoir management and real-time decision-making are major challenges faced by the industry, the data-driven models presented in this paper has great potential to be applied in other recovery projects such as solvent-aided steam injection.

The depositional environment at Statoil's Leismer SAGD demonstration project is dominated by a fluvial-deltaic system with complex geological features which present challenges for SAGD operations. An efficient monitoring tool that could combine geoscience data with production knowledge is vital for optimizing SAGD operational strategies. Time lapse (4D) seismic survey is one such monitoring tool. It can be integrated with wells and production data to prepare and optimize operational strategies. The pattern of growth of steam chamber is critical for well performance optimization. Steam chamber development dictates which reservoir areas are using heat efficiently and contributing to drainage of bitumen and increased production. At Leismer, two 4D monitor seismic surveys have been conducted since the start of operations. The 1 st monitor was acquired in 2012 to observe one year of SAGD performance and the 2 nd monitor in 2013 to observe the performance after 2 years of operation. These surveys have been used to identify challenges to maximizing well pair productivity. Of primary concern is the effective distribution of steam along the entire horizontal section of the SAGD well pair. Other 4D monitoring benefits include, monitoring subcools with respect to reservoir heterogeneities, bottom water communication, steam chamber coalescence, baffle or barrier to steam chamber growth identification. In this study, examples are presented to show how combining production and 4D seismic data has contributed to well pair optimization. Communication with bottom water in the early stages of SAGD, while rare, occurred in specific Leismer well pairs – analyses of production data combined with 4D seismic data helped identify steam loss into bottom water which drove steam injection strategy and improved well productivity. Another Leismer example shows how 4D seismic has been used to confirm a low reservoir roof at the toe of a particular well pair. This knowledge drove a change in injector well completion to optimize production. In the final example, it is shown how 4D seismic has helped analyze and confirm well conformance (heat distribution along SAGD well pairs) which is critical for uniform steam chamber development. Based on Statoil's Leismer experience of applying results of 4D seismic analyses for well optimization, it can be concluded that 4D seismic has proven to be a very useful and value adding monitoring tool.

Numerical tuning has shown to be an effective approach to improve computational performance of simulation models, especially complex large-scaled ones. One purpose of numerical tuning is to find a set of numerical parameters that make the simulation run faster with less material balance error. However, in many cases, these two objectives are conflicting with each other. The most common way to tackle this problem is to create an aggregated global objective function, which is the weighted average of the two objectives, and then try to optimize it using a single objective optimizer. In this study, Multiple Objective Particle Swarm Optimizer (MO-PSO) is applied to conduct numerical tuning on a sub-model of a real field SAGD case study. MO-PSO tries to reduce the simulation run time, and the material balance error simultaneously. The results obtained by MO-PSO are compared with that of single objective particle swarm optimizer (SO-PSO). Results showed that MO-PSO is much more effective and efficient in finding Pareto front compared with SO-PSO. MO-PSO eliminates the usage of any weighting factor, which makes it much easier to set up and avoid biased searching results. In addition, MO-PSO converges to optimal solutions much faster than SO-PSO for this study. SO-PSO with well-defined aggregated weighted global objective function can also find a good Pareto front, but inappropriate weighting factors in the aggregated global objective may lead to biased solutions.

Steam assisted gravity drainage (SAGD) process has been widely used commercially in Western Canada for bitumen production. Improving oil production rate and reducing steam oil ratio has been the focus of the industry. In heterogeneous reservoirs, oil production could be impeded by local steam break through and high liquid level above the other section of the producer. Various completion methods have been proposed to improve production efficiency. Steam splitter is proposed to match steam delivery to reservoir requirement and scab liner may be used in producer to maximize oil production. In general, oil drainage into producer may need to be slowed down at some locations and speeded up at other locations of the well. Non-uniformity of the reservoir pay and quality also has a direct impact on oil production and consequently, it is significant to divert required amount of steam to the desired spots of the reservoir. In this study, we address how to design steam splitter and scab liner in order to optimize SAGD production. Results from reservoir simulation with coupled wellbore hydraulics will be presented to show how a wellbore could be optimized by attaining favorite pressure profiles inside the injector and producer liners. This investigation will also address sensitivities on steam splitter location, size and number of holes in splitter, and size and length of scab liner.

Cyclic steam stimulation (CSS) and steam-assisted gravity drainage (SAGD) are successful commercial methods for enhanced oil recovery (EOR) used in Canada. Although SAGD exhibits higher oil recovery than CSS, it is more sensitive to reservoir heterogeneities such as top water and lean zones. In most cases, these baffles cause channeling in steam injection operations, thus leading to a high steam-oil ratio (SOR) and low injectivity. Therefore, recovering bitumen by using SAGD in a pilot well pad with such baffles in Long Lake has been a considerable challenge. To overcome the aforementioned issues, a hybrid process that combines CSS and SAGD (hybrid CSS/SAGD) is investigated in this study. Hybrid CSS/SAGD takes advantage of the benefits of both methods. During this process, all SAGD wells operate as cyclic injection/production wells. When the wells are shut in for a soak period, the injectivity of SAGD is highly improved. Simultaneously, the producer can also function as a heat source. After good conductivity is achieved between the injector and the producer, oil recovery is improved significantly. This study aims to construct a hybrid CSS/SAGD simulation model for the Long Lake pilot well pad. The simulation is based on a well-defined 3D geostatistical model. By strictly controlling detailed lithological facies, lean zones and top water zones are accurately described in the geological model. These baffles are mainly located in the upper-middle portion of the McMurray Formation. By adjusting relative permeability curves, a SAGD model with a ten-year production history (beginning in April 2003) is history matched well. Based on this model, we then run hybrid CSS/SAGD simulations by changing well constraints from April 2003 with a submodel of well pair 1. Both wells in the well pair 1 operate as producers and injectors. The simulation results show that CSS-SAGD exhibits higher oil recovery and lower SOR than SAGD alone. Moreover, sensitivity analysis on preheating pressure is performed. Based on the net present value, we optimize the steam injection pressure and steam injection rate to establish criteria for each parameter for the pilot well pad.

Bottom water coning has a detrimental impact on the performance of steam assisted gravity drainage (SAGD) causing reduced oil mobility around the producer. It, also, leads to higher steam to oil ratio (SOR). Produced water chlorides concentration can be used as an indication and quantification tool for bottom water coning analysis. The chlorides material balance for bottom water coning analysis depends upon connate water saturation and the concentrations of chlorides in connate water, bottom water and produced water along with the water cut of produced emulsion. Most oil sands are water wet but wettability changes with high steam temperatures and almost all the connate water is drained because of steaming. The safe produced water chlorides concentration, at which no bottom water is produced, depends mainly upon connate water saturation and its chlorides concentration besides water cut and oil saturation. Therefore, careful sampling and laboratory procedures for connate water chlorides measurement is vital in bottom water coning analysis. The mechanical extraction device (referred to as the plunger) was developed mainly to facilitate the recovery of heavy oil and bitumen samples from oil sands cores. In this study, the plunger technology is employed in order to recover water samples to enable measure connate water chlorides PPM (concentration in parts per million). The measured connate water chlorides are also corrected for drilling fluid invasion. A new material balance for bottom water coning analysis is established based on different connate water and bottom water chlorides concentrations and drainage patterns of connate water and oil saturations. The new material balance is used to determine the safe chlorides concentration and applied to bottom water coning analysis in the field application at Leismer SAGD project. Since, the earlier study assumed the same chlorides concentrations of bottom water and connate water, the new measurement technique of chlorides concentration of connate water has led to a better material balance calculation for bottom water coning and data analysis. The new material balance has led to a lower safe chlorides limit. It has, also, helped better optimization of steam chamber pressure and steam injection strategy to avoid negative impacts of bottom water conning to enhance oil production and lower SOR at Leismer.

Athabasca is the largest resource accumulation among three major Canadian oil sands reservoirs. Through more than twenty years of research and studies, Steam Assist Gravity Drainage (SAGD) has been proven as an economic viable commercial method to develop Athabasca oil sands. However, because very few of industrial SAGD projects have matured to date, Wind-Down (WD) and Blow-Down (BD) operation strategies are not well understood and more research and study efforts are needed. In this paper the beginning time and gas injection and production rates in WD and BD with pure methane are optimized for an Athabasca oil sands reservoir with numerical simulation. A simplified economic method and assumptions for optimization are presented and applied in this study. Six type wells are selected to capture the complexity of geological architecture (including pay thickness, and permeability and porosity heterogeneities) and fluid characteristics (thief zones) of the investigated reservoir. The optimization is based on the net values of WD and BD with 2D layer cake simulation models. The sensitivity of the net values to gas and bitumen prices is also analyzed. The strategies to optimize WD and BD for compartment areas or full fields are explored in this paper. For the areas without geomodels, a weighting method is proposed and applied. For areas with geomodels single model simulation is recommended if the areas are not large while the type well/pad methodology is suggested if the compartment areas or full fields are too large to simulate with one model. The results of this study indicate that understanding reservoir geology and optimizing WD beginning time are important and necessary to have positive WD economic values. In some cases compromise has to be made in optimization simulation due to the complexity of reservoir, availability of model and limitation of resources, and a practical optimization method with proper simplifications have to be taken. With the optimized WD and BD operation and management strategies the economy of SAGD projects can be improved significantly.

SAGD (Steam Assisted Gravity Drainage) is an efficient and proven technology to recover vast reserves of Alberta's oil sands. Because of its thermal and compositional effects, numerical simulation of the SAGD process requires extensive computational run time, especially in a history matching framework. Therefore, it is beneficial to use an optimization technique that yields faster convergence and better match-quality solutions. This paper presents a new population-based optimization technique, called differential evolution, in the assisted history matching process. Differential evolution belongs to the class of evolutionary algorithms in the continuous parameter space that has been used successfully in a large range of engineering optimization problems outside the oil industry. Differential evolution converges faster than many other global optimization methods. It requires fewer control variables, is robust and easy to use, and lends itself very well to parallel computing. We applied the differential evolution technique to a SAGD case study to history match saturation and temperature profiles as well as cumulative oil and water production and cumulative SOR. The results show that it is an excellent optimization technique for obtaining multiple good history matched models, which allow the assessment of uncertainty for the forecast stage. The match-quality of the history matched models obtained with differential evolution has been compared to the results of the particle swarm optimization method that is widely used in history matching. The comparison shows that differential evolution offers much better match-quality solutions with much lower number of simulation runs.

Quantitative ranking of different operating areas and assessment of uncertainty due to reservoir heterogeneities are crucial elements in optimization of production and development strategies in oil sands operations. Although detailed compositional simulators are available for recovery performance evaluation for SAGD, the simulation process is usually deterministic, cumbersome, expensive (manpower and time consuming), and not quite suitable for real-time decision making and forecasting. In this paper, Artificial Neural Network (ANN) is employed as a data-driven modeling alternative to predict SAGD recovery performance in heterogeneous reservoirs, an important application that is lacking in existing literature. In this study, numerical flow simulations are performed to construct a training data set consists of various attributes describing characteristics associated with reservoir heterogeneities and relevant production/injection parameters with the corresponding recovery factor as output. The network is trained using the data set to identify all significant patterns and relationships that exist between these attributes and the output parameters. The model is then tested using a verification data set (cases that have not been used at the training stage). Sensitivity studies on network configurations are also investigated. In addition, new modifications are proposed to identify and reduce extrapolations in predictions, which are often considered as major drawbacks in most data-driven modeling approaches. The approach described in this paper can be integrated directly into most existing reservoir management routines. In addition, the technique can be used as a viable tool for analyzing large amount of competitor data efficiently. Given that robust forecasting and optimization of heavy oil recovery processes is a major challenge faced by the industry, the proposed research has great potential to be applied in other recovery projects such as solvent-additive steam injection.

Data driven well models were constructed for a steam injected field producing 800 m3/d of API 8 bitumen from 100+ beam pumped wells. The modelling technique is known as Production Universe(PU) which gives real time estimates of oil and water flows for each of the wells during steady state and transient operations. PU tracks rates when wells are starting-up, closing-in and for transient flow oscillations, resulting in automatic daily reports of total production and deferment (low and off) for each well. The deferments reports drive an exception-based surveillance (EBS) process by flagging the engineers of the largest production gain opportunities. EBS analysis and associated well remedial actions have resulted in an 8% production gain. Continuous estimation of well bitumen and water flows has allowed real time optimization of the entire field. This is achieved by continuously maximizing production within the plant water handling and economic constraints. The system knows how much oil and water the wells are producing and recommends rates for each of the wells to maximize production within the water handling capability. The EBS process has been successfully running for more than a year. The RTO process has just been introduced, hence it is too early to estimate the incremental gain. This paper will detail the surveillance and optimization techniques, learnings and associated work processes.

Every simulation engineer wishes to simulate large full field models, but historically reservoir simulation of the SAGD process has been constrained to single well models up to a single pad. Models of these sizes provide valuable information and have helped to assess the development potential of reservoirs. These models may be used for reservoir management and to support the decision making process for the design of the initial completion, operating strategy, multi-pad wind down evaluations and also qualitatively assess the uncertainty in the SAGD forecast under different geological settings. However, in many cases we are left with the question of how multi-well and multi-pad communications ultimately affect performance at the well pair scale. Due to technological constraints with computer hardware and simulation technology, running extremely large multi-pad models has been until recently largely impractical, especially when trying to run multiple scenarios to better understand the impact of geological and operational uncertainty. In this paper, we present a new and practical workflow that makes running extremely large multi-pad, multi-million grid cell SAGD models a reality. The three major steps of the workflow are 1) Generating simulation friendly geomodels, 2) Use of experimental design and 3D sub-models based on SAGD Performance Index (SPI) for numerical tuning, and 3) Use of 2D cross-sections and SPI to develop dynamic grid refinement parameter values for the full 3D model. All of these steps are aimed at improving the numerical stability and run time of multi-pad SAGD simulation models. A 24 SAGD well pair model with 2.52 million gridblocks was simulated for 10 years of forecast. The reservoir is geologically complex and highly heterogeneous. We discuss some of the important aspects that need to be accounted for when simulating large scale SAGD models. Using this new workflow, the simulation run time was reduced from 42 days to 7 days on 8 CPUs - a 6 times speedup. The resulting run time is short enough to facilitate multi-realization simultaneous runs using 8 CPUs hence maximizing the throughput and minimizing the simulation cycle time. This new workflow can be easily replicated and, more importantly, automated to reduce engineering time requirements.

Stochastic optimization, based on a simulated annealing method, was done to determine the optimum hot water-flooding strategy for recovery of oil from thin (<6 m) heavy oil reservoirs. The results reveal that high injection pressures are critical to a successful hot water flooding strategy. For water temperature during injection, the results show from a thermal efficienty point of view that that it is most efficient to adopt a temperature profile where the injection temperature starts high and ends at low water temperature. Multiple cycles of this profile might be beneficial depending on the reservoir conditions. The lower temperature injection at later stages of the recovery process partially recovers the heat stored in the reservoir matrix and therefore increases the overall heat utilization efficiency. A sensitivity analysis shows that the permeability distribution affects the performance of the hot water flooding process most significantly. The existence of a higher permeability zone in the lower part of the reservoir leads to earlier oil production and water breakthrough. The absolute permeability value has the largest effect on process performance. High permeability was found to lead to more oil and water production in the early stage of operation and achieved the best economic performance. The low permeability case was found to show a slow oil production. Although it has the lowest cumulative injected energy to oil produced ratio, poor oil production made the operation process uneconomic. Keywords: thin heavy oil reservoirs, hot water-flooding, energy to oil ratio, stochastic optimization, simulated annealing

Petrocedeño is a Venezuelan joint venture (JV) between PDVSA, Total, and Statoil. Petrocedeño operates in the San Diego field of the Junín block located in the Orinoco Belt, where over 500 lifted wells currently produce over 120,000 bpd of extra-heavy oil (7-9.5 API) using progressive cavity pumps (PCPs). Petrocedeño has extensively used down-hole sensors to monitor PCP operational parameters, such as velocity, torque, vibration, intake and discharge pressure, and temperature. Timely and proper usage of this data, incorporated with other well information and operational data, improves production optimization using proactive surveillance and diagnostics of underperforming wells. In spite of having process data in hand, it was noticed that additional optimization can be obtained by integrating the data available with articulated workflows. Initially, a pilot was conducted to evaluate technology aimed at optimizing the production of 50 wells through timely identification of underperformance occurrences. This was achieved using: Automated data gathering and integration Automated daily production rate estimation using operational data and artificial neural networks (ANNs) Customized surveillance and diagnostic workflows The technology applied was developed by integrating data and estimating well production rates on an hourly basis. This involved using trained ANNs and a leading production technology platform. In addition, continuous surveillance workflows of operational parameters as well as estimated rates and other production information were implemented on engineers' computers through customized well and reservoir analysis software created during the pilot. After the pilot project, Petrocedeño engineers were able to reduce the time to identify underperforming wells in 20%. The positive results achieved in the mentioned pilot, encouraged the company to implement the system in the whole San Diego field, as well as introducing additional production surveillance and optimization workflows and visualization tools. This paper presents some of the main workflows implemented and the results obtained.

The SAGD process has been utilized for the past 15 years and an efficient steam utilization process provides the best solution to reducing the variable production costs and to increasing the recovery rate. The use of real time downhole monitoring is an effective approach to achieve this optimization. The main objective of steam injection is to decrease the oil viscosity, so it can flow through the horizontal and the vertical sections. The emergence of downhole distributed sensing (DTS) and array temperature sensing with fiber optics technology has led to instrumented wells allowing operators to access data on a real time basis leading to a better control of the operations and the ability to optimize the steam process. Additionally, fiber optics technology allows for the measurement of pressure and temperature on same fiber and in close proximity along the wellbore. In the ultra-high temperature environment the amount of pressure monitoring points are limited and many SAGD operators are asking for at least 3 pressure in-conjunction with the distributed temperature points. The SAGD process has a number of different variables that are dynamic and complex and changing with time. The most important variables of the process include the steam injection rates, subcool and downhole pressure and temperature. One of the key objectives when using the SAGD process is to reduce the SOR to lower the high cost of steam generation. The optimization of the SOR is a process of reducing the steam injection rate and/or increasing the oil production. Since SAGD is a dynamic process this can only be achieved through the use of Real Time Optimization. Once the steam reduces the viscosity and mobility of the oil is achieved, the SAGD well production can be optimize using the nodal analysis technique meaning that an Inflow (very dependent with temperature & viscosity) will be calculated and optimized but it is also necessary at same time to calculate the outflow which is dependent on the type and optimization of artificial lift system that is used. The integration of different measured points of pressure and temperature (40 points of temperature and 2 pressure points) using fiber optic technology along the horizontal section of the producer well with other parameters like wellhead pressures, temperatures and mainly production rates will allow for the optimization of the SAGD Process and better management of the well. This paper will describe in detail the processes that are required to be controlled and the main variables that are needed to be measured and calculate to optimize the oil production on a SAGD well.