Managing Subsurface Uncertainties in Deepwater Facilities Design

Transcription

1 Managing Subsurface Uncertainties in Deepwater Facilities Design By Richard D Souza and John M. Vitucci KBR Granherne 2013 Deep Offshore Technology Conference, The Woodlands, Texas Introduction Deepwater oil and gas facility design is highly dependent on the subsurface data. Production volumes and rate forecasts, fluid compositions, fluid contaminants and other reservoir derived data are key inputs to a facility design. Subsurface data and the information predicted from such data carries an inherent uncertainty that resolves itself only over the production life cycle. Key reservoir data is often developed from a limited number of reservoir penetrations, point measurements inside the wells, and non destructive remote data acquisition. Characterization of the data is further complicated by changes that occur over time in fluid compositions, flow regimes, flowing pressures, and temperatures. Reservoir performance and production forecasts based on this data are usually developed in probabilistic terms to capture the uncertainty and often with an optimistic bias. Facility design requires designers and engineers to use deterministic inputs and develop a design to handle a specified level of production volumes and rates within a limited processing range. Capturing the underlying data uncertainty within the facility design is a key factor in designing and operating a successful deepwater facility. The high cost of building, installing, and operating today s deepwater production facilities makes it essential that such uncertainty can be addressed over the life of the field. The challenge for facility engineers is one of designing facilities that are flexible enough to handle a range of production scenarios rather than a limited production scenario. Subsurface Data and the Facility Design Basis Various elements of the subsurface data set are needed as inputs to a facility design basis. This data can be classified in three main categories: Reservoir geology and geometry Reservoir conditions (pressure, temperature, etc.) Reservoir fluid properties and compositions Reservoir geology and geometry have a significant impact on well count, well locations, and well construction. Well count and locations will have an impact on the hull type, hull size, and facility layout. The pressure and temperature conditions influence flow assurance, operability strategies, pipeline and riser design, and facility pressure and temperature ratings. Reservoir fluid properties include compositions, contaminants, and gas oil ratio (GOR), as well as water, wax, and asphaltene content. Fluid properties affect many different areas of facility design including process design, facilities design and sizing, systems and equipment configurations, materials selection, and operating strategies. Table 1

2 1, Relative Impact of Reservoir Fluid Properties on Field Development Components, illustrates the qualitative impact of reservoir and fluid parameters on several field development components. Subsurface data from all three categories is also used to generate production forecasts, rate estimates, fluid stream compositions, and other time dependent information through flow assurance and reservoir modeling. These forecasts are essential to developing the facility design basis and affect different areas of facility design including flow assurance, pipeline and riser design, process design, facilities design and sizing, and operating strategies. The subsurface data is usually expressed in probabilistic terms to capture the uncertainty in reservoir geology, geometry, and conditions. The uncertainty is expressed in a range of reserve estimates which in turn drive a range of production and rate forecasts. The uncertainty lies not only in the initial forecasts but continues in time as the reservoir is produced and changes occur in fluid stream compositions, oil compositions, pressures, and temperatures. The specification and implementation of different reservoir management practices such as water injection or gas injection further adds to uncertainty in the production forecasts. The presence of CO 2 or H 2 S in the production stream adds more complexity to the fluid stream compositions and production forecasts. Difficulty in developing good production forecasts is illustrated by a recent industry benchmarking study that examined 59 major development projects and found that the forecasted production was not achieved for a majority of the projects. The facility design basis is used to document all design inputs and design assumptions. It includes subsurface data, production forecasts, flow assurance modeling results, and production fluid properties. The design basis must also cover health and safety, environmental, operations, and in some cases, drilling, completion, and workover requirements. Well counts, well locations and subsea production system interfaces will be included. The design basis is usually developed on a deterministic basis by using single point or limited ranges to describe the many inputs needed for design of the facility. Generally, initial conditions are used to develop the design basis and assumptions. These initial conditions are usually based on a mid point or P50 production forecast. This method, while generally accepted in the industry, creates a fundamental problem in describing the subsurface data and information derived from such data (e.g. forecasts). Data sets with a probabilistic basis and a wide range of potential outcomes are described as a single point or by limited ranges that do not fully describe the potential production conditions the facility may handle during operation. However, the method does allow facility design to follow established deterministic and detailed design procedures and methodologies. The inherent weakness in facility designs produced using these methods is that the design is optimal for only a limited range of production scenarios. Limitations in this design method may produce the following results: The facility is undersized or oversized with respect to the production rate during the initial production period, i.e. first few years of production The facility is undersized or oversized with respect to production rate during mid to late field life 2

3 At some point in the field s life, the facility will be undersized or oversized with respect to the systems selected to handle specific production or injection streams (i.e. produced water, produced gas, water injection, gas injection, or flaring) The facility is at risk with respect to handling produced levels of contaminants such as CO 2 or H 2 S The facility will be limited in space and weight capacity, precluding facility expansion for additional processing equipment or well additions (Dry tree or DTU hull concepts). All of the above situations may result in higher operational costs, reduced asset profitability, and reduced availability or operating time. Managing Uncertainty in Deepwater Facility Design The challenge for facility engineers is to design facilities that adhere to deterministic design methods, requirements, and standards but also allows for uncertainty in the underlying design basis and assumptions. The idea of design risk is introduced as risk in designing a facility that is suboptimal over the life of the development due to uncertainty in the design basis and underlying design basis data or assumptions and using limited estimations to represent such data. This design risk has traditionally been addressed by using conservative design assumptions. To date these methods have been relatively successful in deepwater development. It remains to be seen whether this design strategy will remain viable given the increasing cost of today s deepwater facilities from an initial capital investment, operating cost, and total life cycle cost. Price Waterhouse Coopers found that only 2.5% of major projects met all their objectives of scope, cost, schedule, and business benefits. IPA s CEO Edward Merrow, speaking at the 2012 SPE Annual Technical Conference, noted that only 22% of recent large E&P projects demonstrated project performance success. Other studies have shown average return on capital for E&P companies on the order of 7% even though hurdle rates of 15% and higher are used for project sanction decisions. While these studies address overall project performance, the findings capture how uncertainty in the subsurface data can affect overall project performance including facility design. The development of new plays in deepwater such as the Tertiary play in the U.S. Gulf of Mexico and the Pre Salt play in Brazil have introduced a higher level of risk with limited data on which to base decisions. The authors contend there are several strategies for managing design risk during the facility design cycle. They include: Maximizing knowledge related to the design risk Developing a robust design basis and facility requirements Building flexibility into the facility design Design and equipment standardization Aligning the design timeline to minimize design risk As discussed previously, design risk occurs when using single point estimations or limited ranges to describe a much broader range or values or probabilistic estimates. Maximizing knowledge of subsurface data is a key element in managing facility design risk. Reservoir data acquisition methods 3

4 such as additional logging, extended flow tests, pressure monitoring across wells during flow tests, and the drilling of additional wells all contribute to reducing the underlying uncertainty in the subsurface data. Table 2, Strategies for Evaluating Well Performance and Reservoir Uncertainty, illustrates some of the more common methods for addressing reservoir uncertainty. It is recommended the benefit of acquiring such data is evaluated before acquisition is performed given the expense of acquiring such data in a deepwater environment. Such evaluation techniques include options analysis, value ofinformation analysis, and cost benefit analysis. The value of information concept is illustrated in Figure 1, Value of Information for Reservoir Risk Mitigation Strategies, and illustrates the trade off between risk and costs within a risk assessment frame work. Such techniques require a full understanding of the impact on the facility design and associated cost to provide useful decision support information. It is frequently observed that reservoir data acquisition decisions are based solely upon the perceived value of the information in understanding the reservoir with little or no emphasis on facility design. This is partly because such decisions typically occur during the exploration and appraisal phases of development, when there is minimal involvement by facility engineers. The development of a robust design basis and facility requirements is another method in managing design risk. A robust design basis is one that is maintained throughout the design phase, that uses the most recent data, and that describes a variety of potential production scenarios. This necessarily makes the design basis document dynamic in nature until the design basis reaches a point of maturity and can be frozen. Continual updates to the reservoir information and production forecasts by the subsurface engineering teams, typically seen in early stages of project development and associated engineering phases, requires good communication lines to ensure such changes are communicated to the facility engineering team and incorporated into the design basis in a timely manner. Such a design basis allows the facility design team to address changes in the underlying design data and assumptions and adjust facility designs to accommodate a wider range of potential production scenarios. For example, a reservoir with a high level of uncertainty in recoverable reserves or that requires an extended drilling program may be better served with a facility designed for an extended production plateau and having a reduced peak production capacity versus a facility designed to address the maximum potential production rate. As another example, a reservoir having a number of future reservoir management options may be better managed through a facility designed to be easily modified or reconfigured over time as the reservoir management options become better defined. Knowing the data dynamics and the level of uncertainty in the data allows the facility engineering team to have a much richer design conversation with the reservoir engineering team. Building flexibility into the facility design is a well established method to address uncertainty in design risk. Such flexibility should not be confused with adopting a conservative design approach which may lead to a facility having a suboptimal design and a higher cost and complexity than warranted. There are various design techniques that can be used to increase flexibility in the facility including: Increasing space and weight capacity of the hull for future expansion 4

5 Configuring processing systems in a modular or parallel configuration expanding their range of operation; i.e. increasing the turn down or turn up of the processing systems Performing a debottlenecking analysis during the initial facility design phase to determine if there are any systems or equipment that may limit the facility performance under the range of defined production scenarios Increasing the well bay area to accommodate future dry tree facilities or additional riser porches for future risers Configuring the facility layout to accommodate future expansions or heavy lifts onto the facility Building additional accessible tie in points and runways for piping, electrical, and utility systems to accommodate future expansion One or more of the above techniques are usually applicable when designing a deepwater facility. While most are biased towards increasing the facility capacity, these techniques can also be applied in conjunction with initial facility sizing to address uncertainty in the production forecast or well productivity. A development plan may be better addressed by designing a facility that is undersized for a range of production forecasts but can be easily expanded should a higher rate of production be achieved. Alternatively, these situations are sometimes addressed through a phased development plan requiring expansion of an existing facility or the addition of facilities over the life of the field. The use of standardized designs and equipment has proven effective in saving costs, installing and commissioning systems for operations more quickly, and providing improved repair, maintenance, and replacement performance. Standardization can also be applied to managing design risk in conjunction with building flexibility into the facility design. It provides a way to modularize or expand a system by using standard components, which minimizes engineering and installation costs, shortens the cycle time from sanction to commissioning, and promotes operational familiarity. Such standard components or systems, when applied across a portfolio of projects, allows the operator to redeploy such systems or equipment from one asset to another to manage design risk across several projects and potentially lower the overall investment required to develop a portfolio of projects. Many of today s deepwater projects are focused on reducing project cycle time and accelerating first oil. This is sometimes referred to as fast tracking a project: performing the engineering, procurement, construction/fabrication, and installation in a shorter time period than typically required. This type of project acceleration often creates misalignment between design basis quality or maturity and the scheduled work, leading to an increase in design risk. Design assumptions can be introduced that may not accurately describe the level of uncertainty in the actual subsurface data. A potential result of this misalignment is a facility design that is suboptimal once the design basis reaches maturity, leading to potential rework of the facility concept and systems. The design risk may be amplified if efforts are not made to reconcile the facility design to the latest design basis and address the resulting design risk or if there is a resistance to revising the facility design once it has progressed beyond early engineering phases. The use of a systems engineering approach and requirements planning methodology to identify and rank data uncertainties, their effects on facilities design, and develop a schedule for their resolution 5

6 in alignment with the engineering work plan is essential in such situations. In general, the use of such planning systems is considered a best practice within design and engineering efforts. Economic Value and Facility Design Risk Using the net present value (NPV) method to evaluate the economic viability of a project is a generally accepted evaluation method for oil and gas projects. NPV generally rewards projects with higher initial production rates for a given level of capital investment. The NPV method, especially when used with an unrisked input data set, will often validate a facility design that accommodates the maximum production rate when compared to alternative designs. Such facility designs may not address design risk at an acceptable level. The consideration of design risk within the NPV methodology can lead to alternative facility designs that better manage the inherent risk and underlying uncertainty. Options such as sizing the facility for less than the maximum production rate (plateau production), phased development, or future facility modifications can address design risk without significantly affecting the economic value of the development. These designs may better address financial objectives once the facility is in operation; profitability, cash flow, and other operations oriented financial metrics. Understanding the impact of design risk, data uncertainty, alternative facility designs and associated costs on economic value are key components in developing an economically robust facility design. This is an iterative process that should run parallel to design basis development and engineering in the early stages of project development and facility design. Conclusion About 10% of the global oil supply is produced from deepwater fields and their contribution will continue to rise in the future. Uncertainty in the subsurface data will continue to be an important factor in developing deepwater areas as new plays are explored and produced, new offshore regions are accessed, and the industry probes increasing water depths. The facilities needed to produce these fields continue to escalate in cost and complexity. Understanding, communicating and managing uncertainty in the subsurface data will be a key component to the design of robust and flexible facilities for these fields. The authors have attempted to identify potential strategies for managing surface data uncertainty or design risk within the context of deepwater facility design. These strategies are built upon the fundamentals of: Identifying and characterizing uncertainty in the subsurface data and information produced from such data Effectively communicating the data and uncertainty between the subsurface and facility engineering teams Developing a facility design basis and assumptions that capture the uncertainty in an accurate manner Implementing strategies to manage design risk related to data uncertainty in the early stages of facility design Ensuring the facility design balances the project s economic value against an acceptable risk level 6

7 References Field Development Planning and Floating Platform Concept Selection for Global Deepwater Developments (OTC 21583), by Richard D Souza and Shiladitya Basu (Granherne), Offshore Technology Conference, May 2011 Selecting the Right Field Development Plan for Global Deepwater Developments, by Richard D Souza, Shiladitya Basu and Ray Fales (Granherne), Deep Offshore Technology Conference, November 2012 Development of the Brazilian Pre Salt Fields When to Pay for Information and When to Pay for Flexibility (SPE ), by B. Moczydlower, M.C. Salomão, C.C.M. Branco et al., SPE Conference, April 2012 Instilling Realism in Production Engineering: Dos and Don ts (SPE ), by A.K. Rajvanshi, R.G. Meyling and D. ten Haaf, SPE Conference, October 2012 Managing Subsurface Uncertainties in Facilities Design, by P. Boschee, Oil and Gas Facilities, Volume 2, No. 1, February 2013 Built In Project Bias Jeopardizes Project Success, by E. Briel, P. Luan and R. Westney, Oil and Gas Facilities, Volume 2, No. 2, April

8 Table 1 Relative Impact of Reservoir Fluid Properties on Field Development Components Key Reservoir Fluid Parameters Units Estimated Range of Values Process Impact on Key Field Development Components Flow Assurance Secondary Recovery Subsea, Flowlines, Risers Low API Gravity < 20 High Med Low Low High Viscosity cp > 100 cp Med High Med Low Low Shut in Pressure psi < 5000 psi Med High High Low High Shut in Pressure psi > 15,000 psi Low Low Low High Low Temperature F < 150 F Med Med High Low High Temperature F > 250 F (or 300 F) Med Low Med High Low GOR scf/stb < 500 scf/stb Low Med Med Low High GOR scf/stb > 2,000 scf/stb Med Low Med Low High CO 2, H 2 S, Chlorides ppm 20,000ppm; 100 ppm; 100,000 ppm High Low Med High High Asphaltenes % CII > 1 Med Med Med Low High Wax Appearance Temperature F > 95 F Med Med Med Low 8

9 Table 2 Strategies for Evaluating Well Performance and Reservoir Uncertainty Strategy Description Duration Drill Stem Test More Appraisal Wells and Sidetracks Extended Well Test Phased Development (Early Production System) Staged Development Single well producing to MODU, gas flared Drill additional appraisal wells to define extent and connectivity of reservoir Single well producing to production platform Multiple wells producing to mobile production platform; gas exported or injected Bring wells online to a production platform in stages (months) 1 2 per well Relatively low cost ($100M $150M per well); MODU can be used for testing per well Some wells designed as keepers More reservoir data and improved reservoir model 6 12 Improved confidence in well performance and recovery Better definition of reservoir connectivity Pros Cons Examples Some (but insufficient) well performance data Limited well connectivity data Increased cycle time to sanction Limited well performance data months to mobilize production platform Capex in $300M $500M range Significant reduction in well performance and reservoir connectivity risk; Test enabling technologies and completions; Optimize full field development plan to capture reservoir upside. Significant Capex ($1B $3B) outlay 36+ months to mobilize platform Life of field Flexibility to capture reservoir upside Maximize reservoir recovery Largest capital investment and longest schedule to peak production among all options Jack (Lower Tertiary, GOM) Roncador (Campos Basin, Brazil) Cascade & Chinook (Lower Tertiary, GOM) Perdido (Lower Tertiary, GOM) 9

10 Figure 1 Value of Information for Reservoir Risk Mitigation Strategies Risk Cost Increasing Cost Decreasing Risk Value of information Drill Stem Test Appraisal Wells Long-Term Production Test Early Production System Staged Development 10