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.

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) H 2 S & CO 2 , 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...