Integrated Asset Modeling Research Articles

Integrated-Asset-Modeling Approach for Reservoir Management on North Slope

This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 158497, ’Integrated Asset Modeling for Reservoir Management of a Miscible WAG Development on Alaska's Western North Slope,’ by R.D. Roadifer, SPE, ConocoPhillips Alaska; R. Sauve, Schlumberger; R. Torrens, SPE, Schlumberger Middle East; H.W. Mead, SPE, N.P. Pysz, SPE, and D.O. Uldrich, SPE, ConocoPhillips Alaska; and T. Eiben, ConocoPhillips Canada, prepared for the 2012 SPE Annual Technical Conference and Exhibition, San Antonio, Texas, USA, 8-10 October. The paper has not been peer reviewed. An integrated-asset-model (IAM) approach has been implemented for the Alpine field and eight associated satellite fields on the western Alaskan North Slope (WNS) to maximize asset value and recovery. The IAM approach enables the investigation of reservoir- and facilities-management options under existing and future operating constraints. The technology used for managing the fields consists of fullfield compositional reservoir-simulation models for each reservoir integrated with a pipeline-surface-network model and a process facility model. Developing an IAM Reservoir-Management Needs. As with the construction of any reservoir or surface model, the first step in developing an IAM is to define with as much clarity as possible the objectives in undertaking such an endeavor. A detailed list of all possible objectives for creating an IAM covering all possible development and operational situations that might occur would be quite extensive. As is often the case, however, simply identifying the “big rocks” will allow the finer details of those leveraging aspects to be identified and planned for in the development of the IAM. The Alpine field anchors the westernmost oil-production and -processing facility on Alaska’s North Slope (Fig. 1). Discovered in 1994, the Alpine field is in the Colville River delta, 6 miles south of the Arctic Ocean and approximately 70 miles west of the Trans-Alaska Pipeline. The Alpine field began production in November 2000 and continues development today. Subsequently, satellite fields, including the Fiord-Nechelik, Fiord-Kuparuk, Nanuq-Kuparuk, Nanuq-Nanuq, Qannik, and Alpine-Kuparuk, have been brought on line and continue to be developed. Additionally, several fields in the National Petroleum Reserve have the potential to be developed. The common theme across all these developments is that they are or will be produced through the Alpine Central Facility (ACF). The ACF is a single-train processing facility. The only significant fluid that leaves the ACF is the sales oil. All other gas not used for fuel or lift must either be blended for injection as miscible injectant (enriched lean-gas injectant) or be injected as lean gas into two black-start gas-injection wells. All produced water must be reinjected and, for pipeline-integrity reasons, must be segregated from imported makeup seawater used for injection.

Technology Focus: Reserves/Asset Management (December 2013)

Technology Focus Members of SPE continue to share valuable and useful information through the more than 90 papers on reserves and asset management presented at various meetings, symposiums, and workshops this past year. Each and every paper presented has valuable content. The papers I selected represent a very small sampling of the topics associated with the varied issues around reserves estimation and asset management. They were chosen to reflect the variety of topics in this category, with a balance between technical and practical papers, and to recognize the global nature of our industry. As with papers presented in 2012, approximately 10% of the papers from both categories addressed risk in some fashion. Reserves papers continue to focus on the analysis of reserves and resource volumes in unconventional and shale developments as well as estimating recovery from coalbeds. However, with the advancing understanding of estimation practices in these properties, many of the papers focused on practical application of previously presented concepts. The same is true for the regulatory aspect of reserves and resources. The publication of SPE’s Guidelines for Application of the PRMS in late 2011 reflects the maturation throughout the industry of the Petroleum Resources Management System (PRMS) that was developed in 2007. Other regulatory changes, such as the modernization of US Securities Exchange Commission rules in 2009, fostered papers on application of various regulatory and fiscal systems and global market forces. Asset-management papers continue to focus on integration of multiple factors to improve our understanding of the effect of various components of the production process. Most of the papers address modeling in some manner, with many presenting interesting field studies that demonstrate the practical application and value of the approach. As a reservoir engineer at heart, I find it indeed satisfying to see many of these papers include the importance of reservoir management alongside completion and facility management to truly optimize production and recovery. Full-length versions of all papers summarized here are available to SPE members free for 2 months at www.jptonline.org. Anyone interested in investigating any topic relevant to reserves or asset management will find a wealth of useful information at www.onepetro.org, where all papers in the SPE library are available for purchase. Finally, I would like to personally encourage anyone who has developed work that might be useful to the society to consider writing a paper for presentation. Recommended additional reading at OnePetro: www.onepetro.org. SPE 159174 Quantifying Proved Undeveloped Reserves in the Woodford Shale: A Seamless Integration of Statistical Empirical and Analytical Techniques by Madhav M. Kulkarni, Marathon, et al. SPE 162631 How to Apply Stretched Exponential Equations to Reserves Estimation by Randy Freeborn, Energy Navigator, et al. SPE 164301 Building a Large-Scale Sustainable Integrated-Asset Model for Greater Burgan by J. Joslin, AAR Energy, et al.

Technology Focus: Reserves/Asset Management (December 2012)

Technology Focus While preparing for this month’s feature on reserves and asset management, I had the privilege of reviewing more than 100 abstracts from papers presented on the topic over the past year at SPE workshops and conferences. Interestingly, more than 10% of those abstracts addressed risk in some fashion. Integration was another common topic. Finally, I was a bit surprised by the number of abstracts that contained the word “practical” in their title. It struck me as a challenge we all face as part of our daily activities—integrating risk in a practical way. The subject matter of the reserves papers I reviewed was widely varied but extremely useful. Papers addressed practical application of engineering processes to comply with the Securities and Exchange Commission’s 2009 modernized rules, innovative reserves forecasting approaches for unconventional reservoirs, the integration of geostatistics and reservoir modeling in reserves estimation, case studies where reserves were both under- and overestimated, and a variety of other topics. As a topic, asset management also covers a wide range of issues. Papers continued to offer insight into field development optimization, techniques to manage risk, application of project management systems, innovative integrated asset models, and related topics. All of these papers provide in-depth, innovative analysis of issues resulting from the constant growth and development of our industry. Choosing only four papers to be highlighted was a challenge. There were many, many quality papers covering current topics of interest. I tried to select papers that span the interests of the global industry and hit a balance between practical and theoretical information. The papers I selected for additional reading represent interesting case studies of somewhat innovative reserves estimation approaches. I hope you find both the highlighted papers and those for additional reading as interesting and thought provoking as I did. Finally, anyone interested in more in-depth information on either reserves or asset management will find a wealth of additional information at www.jptonline.org, where the full-length papers of those synopsized here are available free to SPE members for 2 months. All of the papers contribute to the advancement of knowledge in our industry, and the authors who contributed to that cause by writing a paper should be congratulated. Recommended additional reading at OnePetro: www.onepetro.org. SPE 151045 Interference Study in Shale Plays by Ayantayo Ajani, The University of Tulsa SPE 143843 Reserves Follow-Up Using an Integrated Deterministic/Probabilistic Approach by Hugo Araujo, Repsol-YPF, et al. SPE 142936 Integrated Asset Model in Camisea, Peru by A. Downie, Baker Hughes, et al. SPE 146788 Field Development Optimization Under Uncertainty: Screening Models for Decision Making by B.A. Ogunyomi, The University of Texas at Austin, et al.

Modeling Facility-Expansion Options Under Uncertainty

Summary During the concept comparison and selection phase of capital projects, decision makers compare development options and select one option to carry forward to detailed engineering. This effort is often complicated by uncertainty in one or more of the critical inputs. Therefore, project teams typically investigate how different assumptions about the uncertainty influence optimal decisions. In cases where initial investment decisions are costly to change, the analysis of facility flexibility increases in importance. The goal is to find the optimal balance between initial capacity and the allocation of space and other resources for future expansion. This paper reports the results of a research project in which the authors develop an integrated asset model for a hypothetical deepwater asset and use the model to investigate various aspects of facility design under uncertainty. The method produces estimates for the optimal initial capacity, the likelihood of expansion, the expected size of expansion, and the willingness to pay for the option to expand. The approach is systematic; the model solves quickly and, thus, enables a wide scope of uncertainty analysis. The graphical output is intuitive and facilitates communication between the facility engineer, subsurface engineers, and management.

Innovative Alternative to Full-Field Compositional Modeling-Case Study of the North Kuwait Jurassic Complex

Summary The North Kuwait Jurassic complex (NKJC) consists of six fields with four potential reservoirs in a naturally fractured Jurassic carbonate formation. Current understanding of the complex has led to 12 subdivisions of the area and potentially 48 separate compartments (segments) in the complex. These subdivisions are defined by fault boundaries supported by a combination of variations in fluid composition, initial pressures, and free-water levels estimated from capillary pressure and log saturation data. Multiscenario production forecasts based on integrated full-field modeling were needed in the process of building a field-development plan (FDP) for the NKJC. An integrated asset modeling (IAM) framework is adopted in which multiple separate reservoir models are coupled through global constraints to meet gas-delivery targets. We present the results of a feasibility study to select the optimal modeling strategy for the complex. We discuss available options to simulate multiple reservoirs, which, although isolated based on the current understanding of compartmentalization, need to meet global production targets. We show that using multiple-reservoirs integration through a controller to couple the separate reservoir models through global production targets/limits provides an optimal simulation framework for the NKJC—the flexibility and computational efficiency of multiple segment models and the comprehensiveness of full-field simulation modeling. The solution uses a black-oil delumping technique to obtain compositional wellstreams while running black-oil simulation models. The feasibility study demonstrated that black-oil delumping enables the composition and component molar rates of a producing well from a black-oil reservoir simulation to be reconstituted accurately. Comparison of results obtained from a compositional model to those obtained from a black-oil model using black-oil delumping shows excellent agreement. The adopted simulation framework provided us with all the benefits of a compositional full-field simulation model while adding two advantages (i.e., computational speed and flexibility). Full computational advantages of black-oil modeling are obtained while preserving the reservoir details required for an accurate prediction of well and reservoir behavior.

CO2 Injection Into Depleted Gas Reservoirs

This article, written by Senior Technology Editor Dennis Denney, contains highlights of paper SPE 123788, ’CO2 Injection Into Depleted Gas Reservoirs,’ by Hrvoje Galic, SPE, Steve Cawley, SPE, and Simon Bishop, SPE, BP, and Steve Todman, SPE, and Frederic Gas, Petroleum Experts Ltd., prepared for the 2009 SPE Offshore Europe Oil and Gas Conference & Exhibition, Aberdeen, 8-11 September. The paper has not been peer reviewed. Depleted gas fields represent an opportunity for CO2 storage. However, low reservoir pressure will present a significant challenge for CO2 injection because of phase-behavior issues. This behavior will constrain injection operating parameters significantly during the early stages of CO2 injection. An efficient way of understanding and possibly resolving flow-assurance problems is to adopt an integrated-asset-modeling (IAM) approach. The IAM recognizes the interaction between all system elements and enables observing the effects of many parameter changes within the whole system. Introduction The case study modeled a BP-operated asset offshore UK in the southern North Sea (UK SNS) for CO2 injection. The modeled CO2 supply would come from burning fossil fuels at a power plant. The resulting CO2 would be captured, dehydrated, compressed, and transported to an onshore pumping facility at approximately 70–80 bar and at ambient temperature (0 to 20°C). Under these conditions, CO2 is in dense liquid phase. An onshore pump then would move the CO2 through a subsea pipeline to an offshore injection facility. From there, it would be distributed to several injection wells (most likely converted from existing production service) and injected into a depleted gas reservoir, which had initial conditions of approximately 27 bar and 90°C. The model assumed an approximately constant CO2 delivery rate of 2×106 t/a for a nominal project life of 20 years, with injection beginning in 2013. Toolkit The injection-modeling toolkit comprised a full-field thermal reservoir model with wells and well-network descriptions, surface-network descriptions, dynamic connections between the reservoir and the well-/surface-net-work models (acting as a data-transfer system and master controller for the integrated full-field model at each forecast timestep), and a consistent set of fluid pressure/volume/temperature (PVT) descriptions between the different models. A major technical challenge was related to the phase behavior of CO2. As Fig. 1 shows, pure CO2 has a triple point of 5.18 bar at −56.6°C and a critical point of 73.8 bar at 31.1°C, enabling three different phase regions to be defined under real reservoir conditions: liquid, vapor, and supercritical fluid. Keeping the CO2 in a liquid or super-critical phase is essential to realize the benefits from the high density of the fluid in terms of bulk-transportation efficiency, injectivity potential, and stored-CO2 mass.

Model centric production management systemsy—opportunity and challenges

While modelling technology is routinely used in oil and gas to control and optimise process facilities and downstream plants, the upstream sector has been slow to adopt model-based control systems, or the models are very coarse and mono-dimensional, reducing confidence in their predictive capability. Further, the models are discipline specific, and the interdependence between the subsurface and the surface is poorly evaluated. This is particularly relevant to coal seam gas developments, where well productivity planning and management needs to be considered in the context of the complete production value chain, and the dependencies between the gathering system and the processing plants considered in optimising the contract nominations. A key breakthrough technology will be a model-based control system that can assimilate all the available data, put the data into context, represent the full value chain, assess the relative impact of the components of the production train, and then assess the decision alternatives in the context of deliverability, processing cost, HSE and market opportunity in sufficient time to have an impact. Advances in data acquisition, transmission and storage, coupled with advances in computational efficiency and software engineering, mean that integrated modelling systems are realisable today.Collaboration between technology providers has delivered software tools that offer a unified perspective across the subsurface, surface gathering systems and facility worlds. The presentation will explore the technology capability, the application opportunity and organisational requirement to fully realise the potential of integrated asset models.

Flow-Rate Estimation From Wellhead-Pressure and Temperature Data

Summary Flow-rate metering has a less-than-satisfactory track record in the industry. Modern sensors offer a solution to this vexing problem. This paper offers two methods for estimating flow rates, predominantly from temperature data to complement-rate measurements. One approach consists of modeling the entire wellbore and requires both wellhead pressure (WHP) and wellhead temperature (WHT), whereas the other uses transient temperature formulation at a single point in the wellbore to compute the total production rate. In the entire-wellbore approach, we use a wellbore model handling steady flow of fluids but unsteady-state heat transfer to estimate production rate, given wellhead pressure and temperature. The model rigorously accounts various thermal properties of the fluid and the formation, including Joule-Thompson (J-T) (Thompson and Joule 1853) heating and/or cooling. In the single-point approach, a single-point-temperature measurement made anywhere in the wellbore, including at the wellhead, is needed to estimate the mass rate at a given timestep. The method entails full transient treatment of the coupled fluid-and heat-flow problem at hand. Examples from both gas and oil wells are shown to illustrate the application of the proposed methodology. Good correspondence between the measured and calculated results demonstrates the robustness of the proposed methods. These methods provide important rate information in various settings. For instance, in mature assets they can fill in the information void between tests or replace suspect rate data. Even well-instrumented wells can benefit because the methods can act as a verification tool, particularly in assets where integrated asset models are used to fine tune rate allocation. In addition, the single-point approach can provide the much needed rate information during pressure-transient tests.

Innovative Implementation of Compositional Delumping in Integrated Asset Modeling

Summary In reservoir simulations, it is common practice to use a limited number of components to describe the reservoir fluids, ranging from 2 (black-oil models) to 5–12 [compositional equation-of-state (EOS) based models]. On the opposite, surface models usually require a greater number of components, typically from 15 to 30. The hydrocarbon components used in surface models are usually lumped into pseudocomponents in reservoir simulations. This disparity in the description of the mixture may become an issue whenever integrated asset models are developed, with the aim of linking surface process and reservoir engineering. In this paper we implemented a consistent integrated simulation workflow from reservoir to process. Process models with detailed compositional formulation were linked to compositionally simpler reservoir models, using Leibovic's delumping scheme (Leibovic et al. 1996, 2000) to convert fluid composition between EOSs with different component numbers. The proposed workflow was applied to simulate two different recovery processes, namely a gas injection below the dewpoint in a sour gas/condensate reservoir; and a miscible gas injection in a near-critical volatile oil reservoir. Results of this implementation, in which detailed components are traced in reservoir model, are compared to integrated simulations in which all stages are modeled using detailed composition.

Technology Focus: Field Development Projects (November 2008)

Technology Focus We all are aware of, and have been affected by, the large swing in oil prices over the past 4 years. I have asserted the importance of evaluation and management of uncertainties when planning the development of a new field or even when revisiting options for mature fields. Higher oil prices may reduce financial risks temporarily for a certain project; however, the complex decisions involved in field development, as well as the many variables to be dealt with, will make almost every project extremely risky. Ultimately, decisions taken in earlier stages, when uncertainties abound, are among the main factors that will determine the level of success of a project. Higher prices may improve the results, eventually, of a project that otherwise would be considered a failure, but the fact is, among the various uncertainties in our business, oil price is paramount. With that in mind, we should give utmost attention in management of uncertainties existing in other factors related to field development, ranging from well drilling to reservoir characteristics. Many in our industry agree, which can be seen by new approaches and recent development of various creative and exciting risk-management tools. Our featured papers this month have some wonderful examples. On a personal note, I would like to mention that after 5 years of writing this column, initially for Subsea Completion and later for Field Development, in October 2008, I will replace Ed May as Chairperson of the JPT Editorial Committee. Ed did a fantastic job of chairing over the last 4 years, and I will do my best to follow his steps, maintaining JPT's status as the primary publication in our industry. I must thank all of you for your constant encouragement and so many thought-provoking emails. Please feel free to continuing writing to me. Thank you. Field Development Projects additional reading available at the SPE eLibrary: www.spe.org SPE 108891 • "Using Geomechanics To Optimize Field-Development Strategy of Deep Gas Reservoir in Saudi Arabia" by Mahbub S. Ahmed, SPE, Saudi Aramco, et al. See JPT April 2008, page 78. SPE 113831 • "The Benefits of Integrated Asset Modeling: Lesson Learned From Field Cases" by M. Rotondi, Eni, et al. SPE 113554 • "Selecting an Optimal Field-Development Strategy for the Vankor Oil Field Using an Integrated-Asset-Modeling Approach" by D.A. Antonenko, OJSC Rosneft Oil Company, et al. Additional reading available at OnePetro: www.onepetro.org OTC 19254 • "An Overview of the Roncador Field Development, a Case in Petrobras Deepwater Production" by E. Bordieri, Petrobras, et al.

Technology Focus: Artificial Lift (July 2008)

Technology Focus Artificial lift plays a fundamental role in several international plays. This month, artificial lift in oil-sand and deepwater developments is highlighted. In both cases, artificial lift is not just accelerating production but is vital to the economic success of the overall development. Today, the Canadian oil-sand developments have followed these different approaches: mining, cyclic steam injection, and steam-assisted gravity drainage (SAGD). As reservoir depth increases, artificial lift, rather than mining, is used to bring hydrocarbons to the surface. While cyclic steam injection and SAGD both push the high-temperature envelope for pumping, new techniques are being proved for this harsh environment. During the production phase of cyclic steam injection, the wells initially flow to the surface. Later production moves into the "flumping" stage (simultaneous flowing and pumping) and then into pumping. While sucker-rod pumping has been dominant, progressing-cavity pumps and electrical submersible pumps (ESPs) are being proved under these extreme conditions. In addition to downhole pumping, there is widespread use of surface multiphase pumps. Technologies that allow pumping high-temperature fluids without cooling represent opportunities to retain heat and improve the overall process efficiency. Surface pumping of a high-temperature multiphase mixture has become an established technology in Alberta. For deep water, artificial lift plays a strategic role, enabling companies that possess advanced technology to develop fields that other companies may determine to be too difficult. The ability to deliver an advanced artificial-lift system is the difference between booking reserves and walking away from deepwater discoveries. Only recently have we seen rapid growth in the application of subsea multiphase pumping. In addition to seafloor boosting, several other artificial-lift methods are applied in deep water, including gas lift and wellbore ESPs. In all cases, the ultimate success of these developments depends on the reliability of the artificial-lift system combined with the cost of intervention. Artificial Lift additional reading available at the SPE eLibrary: www.spe.org SPE 113904 • "Recent Advances and Practical Applications of Integrated Production Modeling at Jack Asset in Deepwater Gulf of Mexico" by Umut Ozdogan, Chevron Energy Technology Company, et al. IPTC 11594 • "A New Approach to Gas Lift Optimization Using an Integrated Asset Model" by Fernando Gutierrez, Schlumberger, et al. SPE 110234 • "Overview of Beam-Pump Operations" by O. Lynn Rowlan, Echometer Company, et al. Additional reading available at OnePetro: www.onepetro.org OTC 18820 • "The Use of Subsea Gas Lift in Deepwater Applications" by Subash S. Jayawardena, Shell Global Solutions, et al. (See JPT June, 2008, page 64.)

Systems Approach Vs. Decision-Tree Analysis: An Offshore Development Decision

A good investment decision-making process is one that is able to assess risk and uncertainty and is able to manage these in a balanced manner. A common practice in the oil and gas industry is to use a decision-tree analysis as a way to incorporate risk and uncertainty in investment decisions. In this article, we introduce an alternative called the system's approach based on a stochastic integrated asset model. The objective is to use the tried and tested decision-tree approach to show the validity of the system's approach and to show that with the incorporation of an infinite number of outcomes, decision-tree analysis results would approximate the results of the system's approach and then demonstrate the superiority of the system's approach. For a hypothetical but realistic example, we demonstrate that the decision-tree results differ by 5% for the mean and 20% for the standard deviation when compared to the system's approach. The decision-tree approach tends to underestimate the low side and overestimate the high side. This difference is due to the fact that the system's approach captures complexity, uncertainty, dependency, and interaction more accurately than the decision tree approach.

Overview: Intelligent Fields Technology (August 2007)

Intelligent Fields Implementation: Why Is It Taking So Long? We have been talking about intelligent fields or digital oilfield matters for some time, and while a significant number of papers have been written on the subject, mainstream application of the technology is patchy. Given the benefits from making better use of our data, why is it that the broad application of the digital oil field has not yet happened? Having been involved over the last 5 years or so, I see five inhibitors.Not enough focus on people and process—It is too easy to see the digital oil field as a new software product. In reality, how data is used in various processes, how workflows could change, and how people interact with data are fundamental to achieving the benefits of the digital oil field.Seeing the digital oil field as a single product—In reality, digital oil field is all about integration: integration of existing software, integration of data sources, and integration of data. This integration requires clarity of the architecture, the data flows, and the output requirements.Aiming for a fully linked integrated model before addressing fundamentals—It is key to start with appropriate instrumentation, data collection, surface control and data acquisition systems, and data conditioning to achieve early benefits from effective visualization and simple analysis. More-complex linking of integrated asset models should wait for "Phase 2."Lack of scalability—Often, assets develop solutions to address the immediate local business need without giving due consideration to being able to scale the solution to other assets. I fully support business-unit-led developments, but a corporate guide is required to ensure that they fit with the overall strategy.Difficulty in sustaining momentum—Enthusiasm, commitment, and resources at build stage can fall away if a key individual moves. Even a fully implemented system can be difficult to sustain if it is not applied consistently across assets. Being aware of these traps and working to address them provides significant opportunity for wider mainstream application of digital oilfield technology over the next few years. Like you, I look forward to it. Intelligent Fields Technology additional reading available at the SPE eLibrary: www.spe.org SPE 104161 "Advanced Production Monitoring" by Martijn Hooimeijer, Shell Global Solutions, et al. SPE 104227 "Smart-Well Completion Utilizes Natural Reservoir Energy To Produce High-Water-Cut and Low-Productivity-Index Well in Abqaiq Field" by Nashi M. Al-Otaibi, SPE, Saudi Aramco, et al. SPE 105374 "A Novel Approach of Detecting Water-Influx Time in Multizone and Multilateral Completions Using Real-Time Downhole Pressure Data," by G.H. Aggrey, Heriot-Watt University, et al. SPE 105618 "Smart Wells: Experiences and Best Practices at Haradh Increment-III, Ghawar Field," by Ibrahim H. Al-Arnaout, Saudi Aramco, et al.

Integrated Flow-Assurance Modeling of the BP Angola Block 18 Western Area Development

Summary As oilfield developments become more challenging and economically challenging, flow assurance has become crucial to the feasibility of projects. Consequently, flow-assurance issues such as hydrates or wax deposition must now be considered early in concept selection. Modern numerical methods, coupled with the latest software engineering techniques, now allow the rigorous calculation of multiphase thermal hydraulic behavior in an integrated asset model (IAM) on time scales acceptable for concept selection. This paper describes the application of a new IAM tool to analyze options for the development of fields in the western part of BP's Angolan deepwater Block 18. Novel aspects include the embedding of field scheduling rules such that the drilling schedules were predicted automatically from the model. In addition, different field architectures were considered including tubing and pipeline sizes, looping of pipelines and subsea multiphase boosting, and the impact on production rates and drilling schedules was quantified. Furthermore, the option to tie back to the planned Greater Plutonio floating production storage and offloading (FPSO) vessel was also modeled with the forecast ullage profile being imposed on production from the new fields. All calculations were performed using rigorous multiphase thermal-hydraulic models allowing flow-assurance constraints to be analyzed simultaneously.

Development and Applications of the Sustaining Integrated-Asset-Modeling Tool

Summary Integrated Asset Modeling (IAM) is a process that combines reservoir, well, and surface-facility models to create a complete system for reservoir and well optimization. This methodology ensures that the interactions among all components are correctly simulated. To realize the full benefit of IAM models, it is critical that changing reservoir and well conditions are entered to keep the models up to date and valid. If an IAM model is not frequently and properly maintained to reflect new conditions, it will rapidly lose its value as it ceases to accurately predict well production rates and pressure drops in the system. An application tool was developed to provide easy updating and maintenance of IAM models for production optimization, surface-network debottlenecking, and production allocation. This tool automates the routine tasks required to update and maintain large-scale IAM models. The unique feature of this tool is its ability to calculate well production rates in almost real time by feeding well operating parameters obtained from the SCADA system into updated well-performance models. These production rates can be used to allocate total volumes measured at gathering centers back to individual wells. In addition, engineers can keep track of well matching parameters, such as productivity indices or skins, in the process of automatically maintaining IAM models using the sustaining integrated asset modeling (SIAM) tool. Trends in these parameters can then be analyzed to diagnose potential well problems and select workover candidates. Application of this tool in business units (BUs) has consistently resulted in a 90% reduction in model maintenance and management time, a streamlined process to maintain and update IAM models, and an improvement in production-allocation accuracy. These improvements have constituted a step change in IAM model application effectiveness across asset teams within BP.

Increasing Confidence in Production Forecasting Through Risk-Based Integrated Asset Modeling: Captain Field Case Study

This article, written by Technology Editor Dennis Denney, contains highlights of paper SPE 99937, "Increasing Confidence in Production Forecasting Through Risk-Based Integrated Asset Modeling: Captain Field Case Study," by L.M. Wickens, SPE, RPS Energy, and G. De Jonge, SPE, Chevron Upstream Europe, prepared for the 2006 SPE Europec/EAGE Annual Conference and Exhibition, Vienna, 12-15 June. To assist probabilistic forecasting and decision making for the Captain North Sea heavy-oil asset, Chevron developed an integrated asset model (IAM). This model includes probabilistic predictions of facilities performance and production, enabling decision-risk analysis for strategic and operational decisions. The IAM includes risk-based oil-, gas-, and water-production forecasts and cash flows. These forecasts take full account of facilities constraints and uncertainties in reservoir and operational parameters through links to decision-risk-analysis software. Introduction The Captain field is a phased development comprising Areas A and B. The field produces high-viscosity oil, with separation taking place on a floating production, storage, and offloading vessel. Critical processing interdependencies, single-train separation with limited redundancy, and gas- and water-handling constraints require facilities downtime with significant effect on production projections. The five producing areas in the field produce from three reservoir units: upper Captain sand (UCS), lower Captain sand (LCS), and the Ross (R). Production consists of the three fluid phases—oil, water, and gas. Produced gas includes gas-cap (GC) and solution gas. Electrical or hydraulic submersible pumps provide artificial lift in every well. All produced water from the field is reinjected into the reservoir, with a portion of it used to power the hydraulic submersible pumps. The dynamic subsurface behavior is simulated with four separate reservoir-simulation models.Area-A UCS plus Area-B GC.Area-A LCS.Area-B eastern extension (EE).Ross. Key to the Captain IAM approach is that a wide range of different reservoir simulations, time dependencies, and production rates from each well can be reconstructed adequately from a two-parameter response surface that itself is not a function of time. This response surface is the phase rate tabulated as a function of total reservoir rate and cumulative production. To investigate this assumption, a response surface for one of the Captain reservoirs was constructed and then used to predict well behavior during different well choke-back scenarios. This response-surface approach can be used to forecast strategies not covered in simulation runs. The IAM has facilitated estimating the effect of uncertainties in reservoir or operational parameters and linking to decision-risk-analysis software. The IAM analysis tool has been used to help with the following.Completing the second development phase.Selecting a concept for a third development phase.Providing credible probabilistic production forecasts.Developing operational and debottlenecking decisions.

A Modern Approach to E&P Asset Valuation, Development, and Decision Making

The development of an oil and gas asset can be one of the most capital-intensive endeavors in the modern business world. The largest energy industry projects may require more than U.S. $15 billion in capital spending. While oil and gas assets normally have long economic lives, they have been characterized historically by low rates of return in comparison with endeavors in other industries. According to a recent report by Goldman Sachs, major oil company properties have an average life span of 21 years, but pay only 10% cash return on capital invested. This level of performance can be attributed to two major challenges faced by nearly all recent E&P projects. First, most include substantial elements of risk and uncertainty, in terms both of commodity prices and estimates of subsurface hydrocarbon reserves and performance. Highly publicized write-downs of booked reserves illustrate the difficulties E&P companies face in this area. The second major challenge is that conventional tools and techniques are unable to model all components of the production system simultaneously—from the subsurface upward through the wells, pipelines, and other surface facilities—across the entire productive life of an asset. Hence, engineers can create and evaluate no more than a handful of potential development scenarios, thus often resulting in suboptimal decisions. Traditionally, E&P companies resort to modeling each component of an asset—the subsurface, surface facilities, and economic outcomes—individually. At the end of this long, tedious process, they combine all the results, hoping they will represent the behavior of a fully integrated asset, which is rather questionable. Alternatively, they may simplify an integrated asset model to a point at which it can be evaluated as a single entity, which makes an accurate representation of predicted vs. actual returns somewhat risky. Both of these approaches evolved 30 to 40 years ago within major oil companies. They were strongly influenced by the hierarchical corporate structures of those days, as well as the limited computational and storage capabilities available at the time. Because most oil and gas companies have been evolving rapidly toward asset-centered organizations, and because dramatic advancements have been made in cost-effective computational power and storage, the methodologies and tools for E&P asset valuation and decision making must be revolutionized to keep pace. Otherwise, the energy industry could squander its most precious resources at a time when global demand is going nowhere but up.