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.

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.

Heavy oils are characterized by high viscosities (i.e. resistance to flow) and high densities compared to conventional oil therefore they require the evaluation of specialized production methods with a systemic approach, since each one is a combination of technologies that works together as a system with the mission of improving recovery of hydrocarbons in an economical, safely and environmentally friendly manner. The probability that a heavy oil well will be constructed and will operate to achieve technical and economical objectives within the framework of an enhanced oil recovery plan is the product of the well construction project and well operational effectiveness. The approach presented in this paper to assess production technologies requires whole life cycle asset analysis to account for uncertainties, risks and reliability in a reservoir-well-surface production system model representing all potential cause effect relations and failure modes to identify the origin of future potential production problems and its classification based on availability of data and the use of analogs for each decision category. The proposed approach has eight processes: 1) Data gathering and reliability analysis, 2) identification of uncertainties, 3) Determination of potential production problems and risks, 4) Identification and analysis of causes of problems at reservoir level, well drainage area and surface 5) Definition of corrective and preventive actions and associated cost and time implications, 6) selection of production technologies options and scenarios, 7) Stochastic modeling of costs, cycle time, resources and system effectiveness for each scenario and 8) Solution design with the required actions for controlling or handling production problems during the life cycle of the field. The solution is modeled with systems dynamics or stochastic simulation to calibrate life cycle costs, benefits, cycle time and required resources and data acquisition to close the loop during the life cycle of the field. Two heavy oil field examples undergoing appraisal and development are used to illustrate how this approach allows for multiple scenarios with combination of options for key decision variables to generate a robust business case during visualization of production response of heavy oil well construction projects.

During the process of Cyclic Steam Stimulation (CSS) variations in reservoir pressure and temperature occur changing the solubility of reservoir rock minerals in the formation water and therefore during production phase, produced water brings valuable information about dynamic characteristics of reservoir rock and fluid. Its analysis may provide an invaluable means for monitoring the reservoir. This paper describes the process of water analysis where results are interpreted on the basis of the principle that the solubility of minerals varies with change in pressure and temperature. This also shows the importance of water analysis as a key tool for reservoir monitoring in fields undergoing cyclic steam stimulation.Water analysis is also used to optimize impact of produced water on Capex and Opex of oil production as water is required to be handled and disposed without impacting the environment, and is applied as troubleshooting tool to identify well problems and to validate log interpretations.Field examples illustrate application of water analysis in i) mineralogical changes that takes place during CCS operation for reservoir monitoring and impact of steam on clays, ii) determining compatibility of injected steam with the formation water and compatibility of effluent with the formation water of disposal well, iii) surface facility design and water treatment before steam generation, iv) reservoir description and computing fluid saturation using resistivity of formation water and v) troubleshooting well problems, e.g. unanticipated water production because of channeling behind the casing and communication between the layers.Paper discusses the importance of water analysis at each stage of CSS operation and its application in reservoir monitoring and describes field experience with water analysis in the surveillance of a CSS project.

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.