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Collaborating Authors
Ortiz-Volcan, J. L.
Opportunity Assessment of a Deep Extra Heavy Oil Green Field: Scenarios for Life Cycle Cost Optimization Under Uncertainty and Risk
Ortiz-Volcan, J. L. (Kuwait Oil Company) | Ahmed, K.. (Kuwait Oil Company) | Azim, S.. (Kuwait Oil Company) | Issa, Y.. (Kuwait Oil Company) | Pandit, R.. (Kuwait Oil Company) | Al-Jasmi, A. K. (Kuwait Oil Company) | Hassan, M. O. (Kuwait Oil Company) | Sanyal, A.. (Kuwait Oil Company) | Taduri, S.. (Kuwait Gulf Oil Company)
Abstract Selecting the optimum combination of technologies is a critical and challenging activity while conducting the opportunity assessment under high levels of uncertainty in a deep (~9000 feet) extra heavy oil green field transitioning between appraisal and development phases. Low mobility requires enhanced oil recovery to be addressed early in the life of the field, so selected wells can be drilled and completed in selected locations to reduce uncertainty about producibility and flow assurance. This paper presents a practical approach to opportunity assessment based on Front End Loading (FEL) methodology, with three major steps: 1. Evaluation of known data, determination of complexities, uncertainties and risks by benchmarking with selected field analogs, 2. Identification of all potential technology options and 3. Definition of feasible appraisal and development scenarios and a high-level road map including estimates of life cycle cost opportunities for optimization. We found reservoir static complexity medium, well complexity low, and reservoir dynamic complexity high. FEL definition indices for reservoir and well indicated low reservoir definition and acceptable index for wells. These complexity and definition indices were used for conducting benchmarking with three analog fields providing references for risks and ranges of production, recovery and total cost. After multidisciplinary analysis with participation of 35 specialists organized into three clusters (subsurface, well and surface), 100 challenges (72 risks and 28 uncertainties) were identified, analyzed and ranked. Assessment of 36 parameters used for Enhanced Oil Recovery (EOR) screening were assessed from uncertainty perspective with preliminary selection of 7 potential EOR methods. Final integration was achieved with identification of 110 technology options for 30 key decisions, finally selecting best suitable options for 4 potential development chronological scenarios. Results are presented in a cost breakdown structure reflecting the most critical cost drivers, where high percentage corresponds to OPEX affected by identified risks and causal maps describes effects on total costs for subsurface, well and surface. We modeled all significant risks by visualizing its impact on total cost and we defined the mitigation actions ranked by risk adjusted stochastic economics performed as input for decision-making. This paper demonstrates that understanding the root causes of high cost per barrel and their relationship with uncertainties and risks during early stages of a heavy oil field life cycle, provides a common language for multidisciplinary cost optimization, and facilitates communication and involvement of all disciplines.
- Asia > Middle East > Kuwait (0.49)
- South America > Venezuela > Zulia (0.46)
- North America > United States > California (0.46)
- South America > Venezuela > Zulia > Maracaibo Basin > Ayacucho Blocks > Boscan Field > Rob-l Formation (0.99)
- South America > Venezuela > Zulia > Maracaibo Basin > Ayacucho Blocks > Boscan Field > Misoa Formation (0.99)
- South America > Venezuela > Zulia > Maracaibo Basin > Ayacucho Blocks > Boscan Field > Icotea Formation (0.99)
- (6 more...)
Abstract This paper presents a practical method for benchmarking heavy oil fields as a tool for identification of opportunities for total cost and production optimization. The method combines actual data from typical heavy oil fields to define reservoir, well and surface complexity indices, for categorizing a subject field and a total cost breakdown model to map potential risks that could cause total cost to increase, potential project/process delay and poor production performance. The benchmarking process consists of four steps: 1) classification of a subject field using Front End Loading (FEL) and complexity indices that account for: a) reservoir structural, stratigraphic, rock, fluid, energy, static and dynamic complexity, b) well complexity and c) surface processes complexity; 2) selection of analog fields within the range of indices; 3) use of causal maps to identify causes of uncertainty and risks that impact capital expenditures (CAPEX), operational expenditures (OPEX), production losses and cycle time; and 4) a total cost stochastic model is used to generate graphs providing the position of the subject field vs. analogs. A total undiscounted cost breakdown structure provided information on the most critical cost drivers, where significant impact corresponded to OPEX. Causal maps described typical total cost drivers for surface and subsurface. Seven most significant groups of risks are modeled to visualize the impact on cost, production losses, cycle time and health, safety and environment with recommended mitigation actions ranked by cost benefit. A database provides information about cost of production (Capex, Opex) from heavy oil fields undergoing cold production and thermal enhanced oil Recovery well-known heavy oil production areas from Venezuela, Canada, USA and Middle East. Heavy oil fields undergoing thermal enhanced oil recovery indicated typical ranges for Opex from 2 to 22 USD/bbl and Total Cost ranges from 10 to a maximum of 40 $/bbl. A key observation is that cost of fuel and power is the largest single OPEX cost for thermal enhanced recovery accounting for about 50%. Significant production losses are associated to failures due to corrosion and blowouts is the most significant HSE risk. The proposed method helps benchmarking total costs in heavy oil fields, which is a task that requires lot of efforts in researching available reliable sources from technical papers, regulatory agencies, and oil industry. Understanding causes of high cost per barrel and their relationship with uncertainties and risks for heavy oil field, is a formidable tool for multidisciplinary cost optimization as it provides a common language that understood by all disciplines involved.
- South America > Venezuela (0.89)
- North America > United States > Texas (0.68)
Abstract At early stages of front end loading (FEL) of steam-based thermal recovery projects, oil companies make critical strategic decisions with limited understanding on how reservoir complexity, uncertainty and risk could affect recovery and economic performance. A solution is to measure front end loading (FEL) and improving it to meet a minimum level of project definition for sound strategic decisions. This paper presents a method to measure FEL by combining complexity and definition rating indices that account for a) reservoir structural, stratigraphic, rock, fluid, energy, static and dynamic complexity and b) definition rating indices and completeness of wells and surface infrastructure. Causal maps guide the assessment of complexity, uncertainty and risk in CAPEX, OPEX and cycle time of typical projects. Sixty-eight factors in eight matrices provide complexity indices and twelve additional factors account for completeness and definition indices for wells and reservoir under static and dynamic conditions. Five hypothetical examples using actual field data from analogs, illustrate how the method works. A causal map describes cause and effect relationships of uncertainty and risk for typical ranges of CAPEX, OPEX and project cycle times which translate into probability of success (POS). Finally, general strategies provide guidelines to reduce uncertainties and mitigate risks. By measuring and improving FEL companies increase the probability of success by mitigating risks such as higher operational expenditures (OPEX) to meet demand of energy, higher capital expenditures (CAPEX) for oversized processing facilities, and deferred production due to failures caused by extreme operating conditions imposed by high temperature, high pressure and corrosive contaminants. This method confirms the benefits of measuring the level of definition of a project, which is a best practice used in aerospace, nuclear, chemical, and other complex industries. It also improves the level of inter-disciplinary communication typical of reservoir-well-surface systems in steam-based EOR projects.
- North America > United States > Texas (0.46)
- Asia > Middle East > Kuwait (0.32)
- Geology > Geological Subdiscipline > Stratigraphy (0.34)
- Geology > Petroleum Play Type > Unconventional Play > Heavy Oil Play (0.31)
Abstract This paper presents a practical approach toward cost optimization of a thermal recovery project in a heavy oil green field in Kuwait. This objective is achieved by understanding total cost breakdown for Capital Expenditure (CAPEX) and Operating Expenditure (OPEX) for all assets (natural and physical) during the economic horizon of the project and the identification of root causes of cost drivers that cause the total cost to increase, potential project delay and poor performance. Complex cause and cost effect relationships are visualized using causal maps and loss causation models for seven most important group of risks during field life cycle. Uncertainties are addressed by comparison with analog fields undergoing Cyclic Steam Stimulation and Steamflood. One-way sensitivity analyses and stochastic modeling of key cost drivers solve a critical uncertainty, lack of OPEX data during commercial operations. Other application includes assessment of risks affecting total cost per barrel and selection of best strategy for risk mitigation with their costs and benefits. A work vs. total undiscounted cost breakdown structure showed the 12 most critical cost drivers, where 70% corresponds to OPEX and 30% to CAPEX, fuel for steam flooding being the highest with 55%. A map with 17 elements was analyzed for associated physical assets, 2 causation maps describes 14 causes of total costs for surface and subsurface, including identification of key uncertainties and risks. Seven most significant groups of risks (total 66 risks) were modeled to visualize the impact on cost, people (health & safety) and environment with all mitigation actions ranked by cost benefit. Understanding causes of high cost per barrel and their relationship with uncertainties and risks for a green heavy oil field, is a formidable tool for multidisciplinary cost optimization as it provides a common language that is understood by all disciplines involved.
Abstract World class oil and gas operating companies apply supply chain management to minimize the risks of investment costs overrun, delays and future higher operating costs of field development projects. Supply chain is a cross-functional approach to plan the flow of goods and services required by a project, based on its front end loading (FEL) specifications, in order to meet business objectives with a successful execution and total satisfaction of the final customers. This paper presents lessons learnt from modeling the supply chain of a steam-based thermal enhanced oil recovery (EOR) heavy oil field development project in Kuwait. A critical building block of a supply chain model is the supply-demand matrix, which is prepared using information about the needs of the steam-based thermal EOR assets (natural and physical) and identification of requirements organized in segments, around capital, technology or manpower categories. A preliminary identification was made about local capabilities to meet these requirements. A work breakdown structure from the front-end engineering design was used to generate a supply-demand matrix with materials including special needs such as energy, water and logistics. The model allows the identification of critical requirements and the information to design alternate options to reduce the risk of lack of supply. One practical result is a map with all required suppliers classified according to the type of goods or services, and the specifications or scope of work for selecting a contracting strategy. Supply chain modeling provides a best practice to meet the demand of goods and services of complex heavy oil steam-based thermal enhanced oil recovery projects. It would help to guide the process of building capabilities in Kuwait oil industry for this type of recovery technologies, which will play a critical role in long-term business strategy.
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Oil sand, oil shale, bitumen (1.00)
- Reservoir Description and Dynamics > Improved and Enhanced Recovery > Thermal methods (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 This paper describes an HSE integrated risk assessment performed by a multidisciplinary team for a Steamflood pilot program in a shallow geologically complex multi layered super-giant heavy oil green field in Kuwait, undergoing first phase of development using field tested Cyclic Steam Stimulation (CSS) during first few years then followed by Steamflood (SF). In the first step of HSE integrated risk assessment methodology, the team stablished the most likely production scenarios during CSS and SF for selected well pattern types and sizes, components of surface infrastructure and production operation modes. To determine the safe distance between wells during drilling operations under current conditions, the team performed a consequence analysis. For each scenario the team defined ranges (minimum and maximum) for well production and injection rates, fluid composition, wellhead temperature, gas oil ratios and other key parameters using data and information from reservoir model, pilots and well designs. To account for the lack of data typical in a green field, the team reviewed well blowout failure modes and frequencies from analog heavy oil fields worldwide. Through internal workshops and using data from analogs, the team did the identification, classification, analysis and ranking of hazards and risks, ending up with a risk breakdown structure (RBS) and a risk assessment matrix (RAM). To identify root causes and their mitigation actions the team prepared cause and effect relationships maps and loss causation models for those risks related to HSE. The outcomes of the assessment are a risk register, quantitative risk assessment, detailed reports and guidelines for the Steamflood pilot program as support to prepare HSE procedures. The team identified 66 risks; classified and ranked them using a risk breakdown structure (RBS) and a risk assessment matrix (RAM) and then selected 28 risks with cause and effect relationships with HSE. The cause and effect relationships maps helped defining the 7 most significant groups of risks (likelihood and impact) in the short, medium and long term: 1) Well blowouts, 2) H2S & CO2, 3) Pattern Type & Size, 4) Heat Management, 5) Non Wanted Fluids & Solids, 6) Reservoir Description and 7) Human Factors. By using loss causation models for each of the seven group of risks, the team established the root causes and risk mitigation options. From the consequence analysis, the conclusion was that 45 meters is the minimum safe distance required between heavy oil wells considering three event scenarios of potential failure cases and the consequences. Finally, to account for the need of critical data related to the most critical HSE risks, the team visualized a Steamflood field test using a small pattern area to reach quickly steam breakthrough and gather a minimum of the needed data in less than 1 year. The HSE integrated risk asssessment methodology presented in this paper is applicable to similar heavy oil green fields to identify potential failure modes associated with well blowouts and other hazards during all phases of thermal operations using data from the subject field or from analogs.
- North America > United States (1.00)
- Asia > Middle East > Kuwait (0.49)
- Asia > Middle East > Israel > Mediterranean Sea (0.24)
- Asia > Middle East > Saudi Arabia > Saudi Arabia - Kuwait Neutral Zone ("Partitioned Zone") > Arabian Basin > Widyan Basin > Wafra Joint Operations Block > Wafra Field (0.99)
- Asia > Middle East > Kuwait > Saudi Arabia - Kuwait Neutral Zone ("Partitioned Zone") > Arabian Basin > Widyan Basin > Wafra Joint Operations Block > Wafra Field (0.99)
- Asia > Indonesia > Sumatra > South Sumatra > South Sumatra Basin > Rokan Block > Rokan Block > Duri Field (0.99)
- South America > Brazil > Parnaiba Basin > Block PN-T-68 > California Field (0.98)
- Well Completion > Completion Installation and Operations (1.00)
- Reservoir Description and Dynamics > Improved and Enhanced Recovery > Thermal methods (1.00)
- Management > Risk Management and Decision-Making > Risk, uncertainty, and risk assessment (1.00)
- Health, Safety, Environment & Sustainability (1.00)
Abstract Assessing heavy oil composition of a green field, with an accepted level of uncertainty so it can be used for refinery capacity planning is a critical challenge. Knowledge of heavy crude oil properties is vital to understand potential adverse impact on process performance and total costs of the whole value chain, downstream (corrosion, catalyst deactivation, fouling and pumping) and upstream (corrosion, fouling, obstruction, producibility, lifting, pumping and transportation). The usefulness of heavy oil properties is highly dependent upon how samples are representing the bulk of hydrocarbon resources that will be developed and the reliability of the sampling procedure which becomes even more challenging when performed in a geologically complex, multilayered, supergiant green field with wide variations of fluid properties vertically and aerially. In this paper we present a field case with the methodology used to design, plan and execute a heavy oil sampling and essay for refinery capacity planning and the lessons learned, in an area of the field representing the first phase of development, in a supergiant heavy oil green field located in north of Kuwait. This heavy oil sampling and essay was planned and executed under high levels of uncertainty, using previous crude assays, PVT data from appraisal and thermal pilot wells and reservoir static and dynamic model. Extensive statistical analysis was applied to understand and map uncertainties related to sampling, completeness and representativeness of laboratory tests vs. accepted international standards. The methodology was applied by a multidisciplinary project team involving specialists from upstream and downstream who performed the work using project management tools to design and implement the execution of sampling in the field. The project took near one year to complete and results are now used for refinery capacity planning at short and midterm. Lessons learned were documented so the future sampling and assays can be improved as data from more wells is going to be available during execution of development phase.
- Energy > Oil & Gas > Upstream (1.00)
- Materials > Chemicals > Commodity Chemicals > Petrochemicals (0.48)
- Asia > Middle East > Qatar > Arabian Gulf > Rub' al Khali Basin > North Field > Laffan Formation (0.99)
- Asia > Middle East > Kuwait > Jahra Governorate > Arabian Basin > Widyan Basin > Ratqa Field > Lower Fars Formation (0.99)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Oil sand, oil shale, bitumen (1.00)
- Reservoir Description and Dynamics > Improved and Enhanced Recovery (1.00)
- Reservoir Description and Dynamics > Fluid Characterization > Phase behavior and PVT measurements (1.00)
- (3 more...)
Abstract This practical method integrates petroleum engineering knowledge, reliability and six sigma tools in a reservoir production system model representing all potential cause effect relations and failure modes to identify the origin of water production and its classification as wanted or non-wanted. A general process is proposed to design a custom made solution for either controlling or handling water production. The solution is modeled with systems dynamics and stochastic simulation to calibrate cost, benefits, cycle time and required resources and data acquisition to close the loop during the life cycle of the field. The method has seven macro processes: 1) Data gathering and reliability analysis, 2) Determination of non wanted water production, 3) Analysis of causes related to mechanical problems in the well, 4) Analysis of causes related to well drainage area, 5) Analysis of causes related to the reservoir, 6) Definition of corrective and preventive actions and 7) Cost, cycle time and resources modeling to design a solution with the required actions during the life cycle of the field. Two examples from two fields in Venezuela are used to describe the application of the method. In these examples cycle time including solution design and implementation is 80 days with a total cost ranging between 300 to 600 thousand dollars. We provide a road map for water production analysis, diagnosis and solution design in oil and gas reservoirs that can be adapted custom- made to any field to address water origin identification, classification as wanted or non wanted water and solution design. This method is non-commercial so it can be used by operators as an alternative to others that could be biased to particular technologies. If used appropriately this method could increase hydrocarbon recovery and reduce risks and costs of environmental impact of water. Finally, this method is a framework for standardization of existing work processes and for integrating the work of all disciplines required for managing water production in an integral way.
- South America > Venezuela (0.34)
- North America > United States (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)
- (21 more...)