Integrated Asset Modeling Research Articles

Solution Enables Well Surveillance in Stacked Reservoirs With Near-Critical Fluids

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper IPTC 23912, “Digital Solution for Well Surveillance in Stacked Reservoirs With Near-Critical Fluid Systems,” by Kristian Mogensen, SPE, Carlos Mata, SPE, and Swarjit Samajpati, ADNOC, et al. The paper has not been peer reviewed. Copyright 2024 International Petroleum Technology Conference. _ Fully compositional integrated asset models (FC-IAMs) are being deployed for an increasing number of fields in the operator’s portfolio. The complete paper describes the development of comprehensive digital well surveillance and field-production optimization for an offshore field consisting of four stacked reservoirs, each containing near-critical fluids. Augmenting the FC-IAM with high-frequency sensor data in addition to proprietary tools to actively monitor well performance helps identify and pursue opportunities to maximize oil production from the field, subject to several system constraints. Introduction The field is approximately 120 km offshore Abu Dhabi. During the appraisal phase, eight wells penetrated the structure and confirmed that the predominant lithologies are composed of dolomite and anhydrite, with subordinate limestone. Production of oil and gas comes from four main reservoirs—A, B, C, and D. The ongoing development centers around miscible gas injection along the crestal part of each reservoir and peripheral water injection. Integrated asset models are one of many key initiatives supporting the operator’s Smart Production Growth initiative. Although the calculations identifying scope for improved production rates are firmly founded in science, substantial digitization efforts are required to automate, streamline, and sustain any such digital solution. Fig. 1 illustrates the fact that company database management plays a fundamental role in providing reliable input to keep models updated and fit for purpose. Fluid-Property Modeling Each of the four producing reservoirs contains near-critical fluids exhibiting a steep compositional depth gradient, which means that the solution gas/oil ratio (GOR) is well-specific and may change over time. The GOR trend is governed by several factors. The lightest part of the fluid column may be produced first because of the improved mobility; this would lead to a decreasing solution GOR over time. However, because of the crestal miscible gas injection, significant swelling will result in a gradual GOR increase followed by gas breakthrough. Solution GOR increases because of gas swelling. It is important to make the distinction between solution gas and free gas because the operational guidelines dictate that the reservoir pressure should be maintained above the saturation pressure and above the minimum miscibility pressure. Well-Test Validation Workflow The compositional framework adds an extra layer of complexity because fluid composition needs to be specified at the well level. A robust procedure is needed to first assign an initial reservoir fluid composition to each well. The second key task is to capture changes over time. The method of solution is referred to as a target GOR calculation. For any given fluid system, an equation of state (EOS) model must be tuned to experimental data to convert compositional information to black-oil properties at specific pressures and temperatures. A so-called anchoring fluid composition initially is assigned to the well, and the EOS can then calculate the resulting GOR and other variables from a simulated separator test. The evolved gas and liquid phases are then recombined mathematically to match a measured GOR from a production test. Matching is an iterative process but normally converges very quickly.

Use of Petroleum Experts Prosper software to obtain additional oil production by analysis of well operation mode

Relevance. Current situation, when an increasing number of subsoil users are moving towards the correct choice of a development system. Therefore, the applicability of integrated asset models is growing exponentially every year. One of the integrated approaches in the field of reservoir–well–ground infrastructure modeling is the use of Petroleum Experts software. This solution has successfully established itself in the international market due to the presence of a large number of various correlations suitable for certain geological conditions, as well as a complex methodology for calculating the tasks. Thanks to the above product, the tasks of calculating the inflow from a reservoir, well flow rate and infrastructure throughput analysis formed a single integrated solution for high-quality modeling of fields/groups of fields. In its turn, the Petroleum Experts Prosper software allows you to evaluate the efficiency of a well, as well as to obtain additional production through optimization calculations on a well model tuned to actual parameters. Aim. To form and analyze the approach to setting up component models (wells) by adapting parameters to actual data in order to obtain additional oil production. Methods. Setting up and adaptation of well models equipped with installations of electrical center pumps, setting up and adaptation of fountain wells, approach to working with a periodical fund, assessment of the effectiveness of the use of Prosper in terms of additional oil production. Results. The results obtained allow us to adapt well models with an accuracy of more than 95%, which simulates the operating mode of the mechanized and fountain mining fund in order to optimize and increase oil production. As a result of the analysis, the criteria for tuning wells were identified, and the minimum necessary set of parameters for high-quality adaptation of models was presented. The effectiveness and accuracy of the method by comparing the actual data on the regimes of the wells with synthetic are also proved. Based on the work done, we can conclude that the use of an integrated model shows us a high convergence with real data, which allows you to conduct optimization calculations with high accuracy with obtaining a result that provides the greatest effect.

New approaches to simplification of integrated asset models

The current trend in modeling of the development of oil and gas fields is the transition from models of individual elements of the production system to complex integrated asset models (IAM) of hydrocarbon production fields. The use of such models is especially relevant for the correct forecasting and management of hydrocarbon production in gas, gas condensate and oil and gas condensate fields, where the parameters of facility infrastructure determine the dynamics of production no less than wells and productive reservoirs. The complexity of integrated asset models is associated with the labor-intensive of its creation and the high requirements for computational and time resources required to create and maintain models. This article proposes approaches to increase the efficiency of calculations of integrated asset models while maintaining the quality of forecasting, which helps to increase the value of modeling and the degree of details of development of project solutions. A study of four integrated asset models configurations was carried out. Firstly, the operating features of a detailed integrated asset model are presented, and then methods for simplifying both the reservoir model and the gathering system model are described. For each model, key characteristics are given, as well as calculation algorithms. Through the example of a gas field, a numerical experiment was performed using all the considered configurations; a comparison of the main technological parameters of development was carried out, which showed similar results for all configurations. Based on the study, a conclusion was made about the possibility of using such simplified integrated asset models to perform operational, including multivariate calculations in addition to detailed integrated asset models.

Technology Focus: Reserves Management (December 2023)

Time and time again, I meet industry specialists who should have a detailed knowledge of the Petroleum Resources Management System (PRMS) and handling uncertainty in the inputs for resource assessment or to the rest of the evaluation work flow. Often, however, the understanding of PRMS basics, application, or reporting seems to be lacking. This year’s Reserves Management papers are chosen to revisit some basics around both reserves delineation and asset management. Paper SPE 210358 reminds evaluators to use multiple approaches (triangulation?) in the presence of uncertainty to derive the best estimates for Proved Reserves, but this should apply equally to all resource assessment. It also stresses the requirement, sometimes overlooked, to ensure that the approach/technology is proven and calibrated to work in the exact situation, both subsurface and surface. Uncertainty is inherent in our business, though difficult to communicate, and we should do all we can to incorporate it correctly. Also looking at the effect of uncertainty, paper SPE 213385 focuses on Monte Carlo simulation for commercialization around well performance in unconventional settings, but its application can be to any technical or commercial uncertainty. Too many people overly focus on precision (the decimal places in the answer) but lose focus on accuracy or usefulness of the work. Uncertainty can lead to errors in the first, and certainly the second, significant figure, thus invalidating all the misleading precision. Analysis should model the parameters to include the true state of knowledge. It seems ever more difficult to avoid discussions of carbon capture and storage (CCS), a major challenge facing the industry, especially when trying to monetize massive high-CO2 fields. Paper SPE 214359 discusses how the Malaysian authorities and Petronas seek to manage and incentivize CCS in their domain and achieve socially desirable goals with improved efficiency—specifically, centralizing the disposal of CO2 through an organization taking the separated gas from the producers. It creates efficiencies through scale and targeted regulatory incentives that do not need to be applied to the direct producers. It reminds me of how liquefied natural gas facilities often are commercially separated from the upstream to manage project financing and improve project feasibility. The recommended additional reading papers delve further into PRMS disclosure details and implications; how allocating volumes (and costs) for incremental decisions can change the optimal outcome; some more around how volumes and costs flow through to economics and how this can be an issue in complex fields; and, lastly, an unusual proposal of uncertainty around the 1P, 2P, and 3P values. Certainly, they remind the reader to consider the effect of uncertainty on volumetrics and categorization. Recommended additional reading at OnePetro: www.onepetro.org. SPE 209695 Can Reasonable Certainty Be Assessed From Disclosed Proved Reserves Revisions? by Enrique Morales, ISVA Oil and Gas Consultancy, et al. SPE 211458 An Algorithm for Calculating Incremental Production in an Integrated Asset Model by Peter Richard Paul Cunningham, Serafim, et al. SPE 212826 Impact of Financial Assumptions on Economic Viability of Incremental Projects by M.A. Mian, O&G Knowledge Sharing Platform SPE 209217 Probabilistic Reserves Categorization Using Percentiles of a Single Probability Distribution by M.A. Mian, O&G Knowledge Sharing Platform

Technology Focus: History Matching and Forecasting (April 2022)

This year’s history matching and forecasting selections, made by reviewer Gopi Nalla of DeGolyer and MacNaughton, reflect the importance of accurate and innovative methodology in the approach toward development of unconventional or challenging plays, from tight oil to highly heterogeneous gas fields to coalbed methane. The authors of paper URTEC 208352 evaluate and compare the performance of rate-normalization and pressure-deconvolution techniques for both synthetic and tight-oil examples. While the synopsis is devoted mostly to the authors’ work in applying these techniques to synthetic cases, much of the complete paper is devoted to tight-oil examples. Ultimately, the authors recommend the pressure-deconvolution approach generally. In paper SPE 207933, the authors apply an integrated approach of using reservoir pressure/gas compressibility (P/Z) calculations to obtain a field gas initially in place (FGIIP) estimation that is then incorporated into an integrated asset model. The technique is applied to a giant onshore gas field. The authors conclude that the new FGIIP estimation can be applied as a reference to re-review the static modeling legacy and to narrow static modeling uncertainties, leading to reliable forecasting and more-efficient field development. An application of the iterative ensemble Kalman smoother to a scenario involving horizontal coalbed-methane wells for a low-permeability field in Australia is the subject of paper URTEC 208291. A forecast study was conducted to validate the history-matched ensemble, with the results showing a good match of 12 months of the new production data not used in history matching, highlighting the robust prediction capabilities of the presented approach. SPE papers continue to a be a vital resource for industry professionals; arguably, such work is more important than ever as the industry and the world adjusts to new modes of collaboration and realities in both the office and the field. I invite you to read the full text of these papers on OnePetro and to find further recent works that advance the literature of this specialized but critical Tech Focus topic. Recommended additional reading at OnePetro: www.onepetro.org. URTEC 208361 - Effect of Relative Permeability on Modeling of Shale Oil and Gas Production by Hamid Behmanesh, University of Calgary, et al. SPE 204835 - Successful Case Study of Machine-Learning Application To Streamline and Improve History-Matching Process for Complex Gas-Condensate Reservoirs in Hai Thach Field, Offshore Vietnam by Son Hoang, Bien Dong Petroleum Operating Company, et al. SPE 207855 - Unleashing the Potential of Relative Permeability Using Artificial Intelligence by Abdur Rahman Shah, Schlumberger, et al.

Material-Balance Method With Static Modeling Helps Generate Reliable Forecasting

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 207933, “The Integration of Analytical P/Z Material-Balance Method With Static Modeling and Integrated Asset Model To Generate Reliable Forecasting for a Giant Onshore Gas Field in Abu Dhabi,” by Bondan Bernadi, Yuni Budi Pramudyo, and Fatima Omar Alawadhi, ADNOC, et al. The paper has not been peer reviewed. Reliable field gas initially in place (FGIIP) estimation is achieved by performing multiple reservoir-pressure/gas-compressibility (P/Z) calculations. Several scenarios are developed in the complete paper by defining key areas based on permeability variation, areal distribution, and pressure/volume/temperature (PVT) behavior. The best FGIIP estimation is then fed back into the static model to generate numerous realizations considering the static uncertainties to produce the same FGIIP. A giant onshore gas field is highly heterogeneous, with permeability, lateral composition variation, and dynamic interaction between wells. Incorporating the correct estimation of FGIIP into the integrated asset model (IAM) has helped yield reliable forecasting and has enabled more-efficient field development. Introduction Reservoir A produces onshore from a limestone formation in the lower cretaceous horizon. The structure is a simple dome that is somewhat steeper on the eastern flank, with two extended noses in the south and southeast directions. The total thickness of this zone decreases down the flanks but more rapidly in the northeast direction. Formation porosity is in the range of 10 to 25%, with a permeability range of 0.5 to 88 md. The original reservoir pressure is approximately 4,300 psia, with a reservoir temperature of 260°F. The field has been producing in depletion mode for more than 30 years, flowing into one common surface network with other depletion reservoirs. More than 100 wells had been drilled in the gas-cap zone, with an average gas-production rate of 15–20 MMscf/D per well. Because of the depletion scheme applied since the beginning of field life, gas-production decline is inevitable. The focus of the complete paper mainly relates to the gas-cap zone.

Technology Focus: Mature Fields and Well Revitalization (January 2022)

The highlight of this year’s feature on mature fields and well revitalization has been the increasing use of artificial intelligence (AI) and machine learning (ML) for analysis of mature assets. Many paper proposals studied using AI/ML techniques cover a wide range of topics including optimization of production operations, faster reservoir characterization of mature fields, and improvement of ongoing waterfloods. In fact, increased use of AI/ML has been a recurring theme in many of the areas covered in the Technology Focus sections of 2021’s JPT issues. There has also been a focus on implementing novel technologies such as the use of autonomous inflow-control devices in horizontal wells to suppress ingress of unwanted fluids in thin-oil-rim reservoirs, using integrated-asset models for debottlenecking, and the use of cased-hole logging in old wells to look for missed opportunities. After serving for 5 years, my time for writing this column is coming to an end. It has been a highly rewarding exercise going through the paper proposals, experiencing the hard work put in by industry professionals to arrest inevitably declining production from mature assets by putting together ideas in diverse fields such as reservoir management, production optimization, drilling, and formation evaluation to produce the extra barrel from mature brownfields. Out of the 314 paper proposals studied in these 5 years, 137 (44%) dealt with waterflood improvement, reservoir characterization, and simulation, and approximately 78 papers (25%) dealt with well revitalization involving workover drilling, completion, and stimulation. Sustaining production from mature brownfields challenges the best from each discipline, and significant success stories have come from a genuine collaboration of ideas across disciplines from subsurface to engineering. Recommended additional reading at OnePetro: www.onepetro.org. SPE 206195 - Data-Driven Optimization of Intermittent Gas Production in Mature Fields Assisted by Deep Learning and a Population-Based Global Optimizer by Javier Fatou Gómez, TNO, et al. SPE 205546 - Role of Geomechanics in Identification of Possible Mechanisms for Nonproductive Time and Improving Drilling Operations in a Mature Field in Offshore Sarawak, Malaysia by Avirup Chatterjee, Baker Hughes, et al. SPE 206303 - Combining Capacitance Resistance Model With Geological Data for Large Reservoirs by Davud Davudov, Resermine, et al.

Technology Focus: Field Development (September 2021)

The digital transformation that began several years ago continues to grow and evolve. With new advancements in data analytics and machine-learning algorithms, field developers today see more benefits to upgrading their traditional development work flows to automated artificial-intelligence work flows. The transformation has helped develop more-efficient and truly integrated development approaches. Many development scenarios can be automatically generated, examined, and updated very quickly. These approaches become more valuable when coupled with physics-based integrated asset models that are kept close to actual field performance to reduce uncertainty for reactive decision making. In unconventional basins with enormous completion and production databases, data-driven decisions powered by machine-learning techniques are increasing in popularity to solve field development challenges and optimize cube development. Finding a trend within massive amounts of data requires an augmented artificial intelligence where machine learning and human expertise are coupled. With slowed activity and uncertainty in the oil and gas industry from the COVID-19 pandemic and growing pressure for cleaner energy and environmental regulations, operators had to shift economic modeling for environmental considerations, predicting operational hazards and planning mitigations. This has enlightened the value of field development optimization, shifting from traditional workflow iterations on data assimilation and sequential decision making to deep reinforcement learning algorithms to find the best well placement and well type for the next producer or injector. Operators are trying to adapt with the new environment and enhance their capabilities to efficiently plan, execute, and operate field development plans. Collaboration between different disciplines and integrated analyses are key to the success of optimized development strategies. These selected papers and the suggested additional reading provide a good view of what is evolving with field development work flows using data analytics and machine learning in the era of digital transformation. Recommended additional reading at OnePetro: www.onepetro.org. SPE 203073 - Data-Driven and AI Methods To Enhance Collaborative Well Planning and Drilling-Risk Prediction by Richard Mohan, ADNOC, et al. SPE 200895 - Novel Approach To Enhance the Field Development Planning Process and Reservoir Management To Maximize the Recovery Factor of Gas Condensate Reservoirs Through Integrated Asset Modeling by Oswaldo Espinola Gonzalez, Schlumberger, et al. SPE 202373 - Efficient Optimization and Uncertainty Analysis of Field Development Strategies by Incorporating Economic Decisions in Reservoir Simulation Models by James Browning, Texas Tech University, et al.

Technology Focus: Offshore Facilities (September 2021)

For this feature, I have had the pleasure of reviewing 122 papers submitted to SPE in the field of offshore facilities over the past year. Brent crude oil price finally has reached $75/bbl at the time of writing. So far, this oil price is the highest since before the COVID-19 pandemic, which is a good sign that demand is picking up. Oil and gas offshore projects also seem to be picking up; most offshore greenfield projects are dictated by economics and the price of oil. As predicted by some analysts, global oil consumption will continue to increase as the world’s economy recovers from the pandemic. A new trend has arisen, however, where, in addition to traditional economic screening, oil and gas investors look to environment, social, and governance considerations to value the prospects of a project and minimize financial risk from environmental and social issues. The oil price being around $75/bbl has not necessarily led to more-attractive offshore exploration and production (E&P) projects, even though the typical offshore breakeven price is in the range of $40–55/bbl. We must acknowledge the energy transition, while also acknowledging that oil and natural gas will continue to be essential to meeting the world’s energy needs for many years. At least five European oil and gas E&P companies have announced net-zero 2050 ambitions so far. According to Rystad Energy, continuous major investments in E&P still are needed to meet growing global oil and gas demand. For the past 2 years, the global investment in E&P project spending is limited to $200 billion, including offshore, so a situation might arise with reserve replacement becoming challenging while demand accelerates rapidly. Because of well productivity, operability challenges, and uncertainty, however, opening the choke valve or pipeline tap is not as easy as the public thinks, especially on aging facilities. On another note, the technology landscape is moving to emerging areas such as net-zero; decarbonization; carbon capture, use, and storage; renewables; hydrogen; novel geothermal solutions; and a circular carbon economy. Historically, however, the Offshore Technology Conference began proactively discussing renewables technology—such as wave, tidal, ocean thermal, and solar—in 1980. The remaining question, then, is how to balance the lack of capital expenditure spending during the pandemic and, to some extent, what the role of offshore is in the energy transition. Maximizing offshore oil and gas recovery is not enough anymore. In the short term, engaging the low-carbon energy transition as early as possible and leading efforts in decarbonization will become a strategic move. Leveraging our expertise in offshore infrastructure, supply chains, sea transportation, storage, and oil and gas market development to support low-carbon energy deployment in the energy transition will become vital. We have plenty of technical knowledge and skill to offer for offshore wind projects, for instance. The Hywind wind farm offshore Scotland is one example of a project that is using the same spar technology as typical offshore oil and gas infrastructure. Innovation, optimization, effective use of capital and operational expenditures, more-affordable offshore technology, and excellent project management, no doubt, also will become a new normal offshore. Recommended additional reading at OnePetro: www.onepetro.org. SPE 202911 - Harnessing Benefits of Integrated Asset Modeling for Bottleneck Management of Large Offshore Facilities in the Matured Giant Oil Field by Yukito Nomura, ADNOC, et al. OTC 30970 - Optimizing Deepwater Rig Operations With Advanced Remotely Operated Vehicle Technology by Bernard McCoy Jr., TechnipFMC, et al. OTC 31089 - From Basic Engineering to Ramp-Up: The New Successful Execution Approach for Commissioning in Brazil by Paulino Bruno Santos, Petrobras, et al.

Utilization of grey madm methodology in technology attractiveness assessment: a case study in upstream industry

Technology assessment help managers to accomplish an overall evaluation of technologic options and to identify investment priorities. Making such priority has become of great importance owing to ever-increasing costs of technological research and development and resource scarcity. Technology Attractiveness Assessment, as a primary step of Technology assessment process, has been considered in this paper. Based on the multi-criteria decision-making approach and because of the inherent uncertainty in the preference information on attributes, a Grey-MADM based methodology has been utilized in this paper to assess the technology attractiveness and rank the upstream industry technological options. Its application to a real case problem has been described step by step. The results of the case study showed that "Nano Coating for Drilling Tools", "Petroleum Systems Model Building" and "Integrated Asset Modelling" are the most attractive upstream technologies.

Technology Focus: Intelligent Operations (May 2020)

Intelligent operations have been a focus for many parts of our industry for close to 2 decades. Our understanding of what this means, how it can be realized, and the boundaries of what is possible have advanced significantly in this period. Initial industry focus was around acquiring more real-time data (e.g., pressures and temperatures) along the flow path from reservoir to the point of sale and optimizing overall field performance. From the initial, simpler integrated-asset models, we have advanced to integrated work flows that enable optimal management of very large and complex fields and their equally complex surface facilities (papers SPE 196564 and SPE 197361). Another area of focus was using smart well completions to optimize production and injection reactively and proactively. Downhole technology and capabilities have matured significantly since the original installations in the late 1990s. The first paper highlighted here, SPE 195935, describes learnings from more than 100 intelligent well completions in presalt fields located in the ultradeepwater Santos Basin offshore Brazil. The industry continues to push the boundary on how this technology can be applied further (papers SPE 195850 and SPE 195361). The future of intelligent operations in our industry is being driven by advances from other sectors that have been embraced for petroleum applications. Foundational changes already taking place include advances in the type and volume of data being acquired (e.g., distributed-temperature- and distributed-acoustic-sensor data through fiber) and how the data are used. Paper SPE 194127 describes how smarter gauges with microelectromechanical-system inertial measurement sensors can enable more-accurate wellbore-trajectory estimation. We are leveraging the Internet of things, artificial intelligence, and machine-learning technologies capable of handling large volumes of data to analyze and optimize all aspects of upstream operations in real time, as seen in the next two papers highlighted in this section. In paper SPE 194084, electrical engineers, computer scientists, and drillers collaborate to use deep-learning techniques for real-time classification of cutting volumes at the shale shakers using a video feed from the rig. All of the technology advances also come together in paper OTC 29531, where live data streams from multiple sources, including wave states, motion sensors, and pressure and temperature gauges, are used with machine learning to develop a live fatigue counter for the risers on a floating production, storage, and offloading vessel and establish that facility life extension is possible. However, especially in these uncertain times, frequent hand washing with soap might indeed be the most intelligent operation of all. Stay safe!

ПОВЫШЕНИЕ ЭФФЕКТИВНОСТИ ПРОЕКТИРОВАНИЯ РАЗРАБОТКИ НОВОГО ГАЗОКОНДЕНСАТНОГО АКТИВА МЕТОДАМИ ИНТЕГРИРОВАННОГО МОДЕЛИРОВАНИЯ

Актуальность исследования обусловлена необходимостью использования комплексного подхода по интегрированному моделированию при проектировании разработки новых газоконденсатных активов Крайнего севера. На сегодняшний день в России наблюдается устойчивая тенденция разработки месторождений в пределах Арктических регионов Западно-Сибирской нефтегазоносной провинции. Добывающие компании, работающие в пределах этой территории, рассматривают запасы углеводородного сырья Крайнего севера как перспективный вариант для компенсации падения добычи в ближайшем будущем. Стратегическое планирование процесса разработки является первостепенной задачей, которую трудно выполнить на высоком уровне без применения современных подходов по математическому моделированию. При разработке таких активов цена ошибки кратно возрастает в связи с высокими геологическими, технологическими и экономическими рисками. Поэтому в работе детально рассмотрен подход по интегрированному проектированию разработки газоконденсатных активов с применением интегрированной модели как ключевого элемента, позволяющего повысить точность стратегического планирования. Цель: доказать эффективность от применения интегрированного моделирования при проектировании разработки автономных газоконденсатных месторождений Крайнего севера. Объект: новые неразрабатываемые газоконденсатные активы Крайнего севера. Методы: индивидуальный подход при моделировании процесса добычи и подготовки углеводородного сырья, интегрированное моделирование процесса разработки газоконденсатных месторождений Крайнего севера. Результаты. Разработана этапность расчетов на интегрированной модели при проектировании разработки газоконденсатных активов Крайнего севера. Описан индивидуальный подход к применению моделей добычи и подготовки углеводородного сырья. Доказана эффективность от применения интегрированной модели.

Integrated Asset Model Enables Simulation of Complex, Multifield Asset

This article, written by JPT Technology Editor Judy Feder, contains highlights of paper SPE 192603, “New-Generation Integrated Asset Modeling: High-Resolution Reservoir Multimodels Coupled With an External Steady-State Network Solver,” by Pietro Selvaggio, Fabrizio Freni, Roberto Rossi, Danielle Christian Di Giorgio, and Ivan Colombo, Eni, prepared for the 2018 Abu Dhabi International Petroleum Exhibition & Conference, Abu Dhabi, 12–15 November. The paper has not been peer reviewed. This paper presents an innovative application of the integrated-asset-model (IAM) approach to simulate a surface network collecting many complex fields and multiple constraints. The use of a last-generation high-resolution reservoir simulator (HRRS) made it possible to perform the reservoirs-network coupling directly by a field-manager (FM) process, which acts as an orchestrator for a variety of reservoirs and network-simulation instances. No third-party controller application was required because the FM of the HRRS directly managed the communication between the different reservoir models and the surface-network model. Introduction The ability to predict reservoir behavior has always played a fundamental role in the definition of a development project, and numerical simulation can be considered the first tool for a quantitative evaluation of reservoir performance. In the past decades, improvements in information technology have increased calculation capabilities, enabling development of dynamic simulators able to solve for more-complex reservoirs and dynamic processes. During the production-forecast phase, for example, technical constraints must be considered, primarily related to hydrocarbon rate (i.e., gas sale limitation, gas/water injection limits, flaring policies, limitations in export pipelines, and treatment capacity) and pressures (i.e., minimum required pressure to reach a treatment, separator working conditions, and export-pipeline design). The increasing development of fields located in remote or offshore areas and the growing interest in near-field exploration make it necessary to exploit common surface networks. In these situations, the previously mentioned technical constraints and limitations must be defined for the entire asset, thus accounting for the intrinsic connection between fields that become interdependent. With standard standalone reservoir simulation no longer able to identify and address these kinds of problems and constraints, integrating the reservoir and network is key to an optimized development strategy. The IAM brings together reservoir, well, and surface-facility models in a unique system for reservoir and well optimization. This methodology ensures that the interactions between all the components are accounted for correctly and, by coupling dynamic reservoir and facilities models into a single IAM, addresses backpressure, the mixing of different fluids, and flow assurance. Currently, two approaches are avail-able to perform the integration. The first is based on combining the reservoir models with a dedicated network simulator with optimization capabilities in a fully integrated model. The second approach is focused on using the capabilities offered by some commercial simulators in which the network solver, on the basis of precomputed hydraulic tables, is included in the simulator together with some features of reservoir coupling.

Investigating the efficiency of gas re‐injection process of an oil field using combined integrated field simulation and intelligent proxy model application

AbstractIntegrated asset modelling is a novel method to overcome the limitations associated with using individual models. This method integrates all the individual models of a field into a single model that relates all the sub‐models using proper boundary conditions. Reservoir, wells, surface, and economic models of an oil reservoir, under gas re‐injection, are integrated. The main goal of this study is to propose a novel approach in integrated asset modelling. An integrated model of a field is used to study how gas must be distributed among injection wells. Another aim of this study is to understand the effects of 4 input parameters on the Net Present Value (NPV) of the field. The input variables are: oil production rate, gas injection rate, and the distribution of gas between injection wells. A comprehensive model of a field was built. Using the experimental design results, a neuro‐fuzzy logic network was developed. The proxy model predicted the simulation outputs with a reasonable accuracy. The effects of input variables were studied. Oil production has an optimum value of 6050 STBD per well. The optimum fractions of injected gas for injection wells 1 and 2 are 0.4 and 0.6 of total injected gas, respectively. This means that 40 % of the total injection gas must be injected to well 1 to have the maximum NPV. The greater the gas injection rate, the higher NPV is.

Well-Performance Calculations for Artificial-Lift Screening

This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 181344, “Well-Performance Calculations for Artificial-Lift Screening,” by P.A. Kefford, SPE, and M. Gaurav, Integrated Production Technologies, prepared for the 2016 SPE Annual Technical Conference and Exhibition, Dubai, 26–28 September. The paper has not been peer reviewed. Having the means to evaluate different forms of artificial lift efficiently early in the planning cycle significantly improves the ability to influence other planning decisions such as well count, well design, and facility capacities and to realize the potential benefits made available by new artificial-lift technologies. This paper focuses on a fit-for-purpose methodology to evaluate well-production performance for a wide range of artificial-lift techniques. Introduction In a typical artificial-lift selection process, the first stage, screening, involves considering a wide range of artificial-lift techniques and their suitability regarding fluid types, reservoir properties, and operating environment. Once a short list of feasible lift techniques is determined, a more-in-depth comparison of these options is undertaken by forecasting the performance over the life of the field. Options for generating such forecasts range from simple spreadsheet calculations to fully integrated asset models coupling detailed reservoir, well, and facilities models. The choice of forecasting technology depends largely on the field size and complexity. The next step is to perform an economic analysis to guide the ultimate selection of the preferred lift techniques. The economic analysis ac-counts for the capital and operational expenditure associated with each lift technique and uses the production profile to generate net-present-value estimates. The initial screening of artificial-lift technologies involves consideration of multiple criteria that will have varying degrees of significance depending on the specific reservoir properties and operating environment. Common artificial-lift screening criteria are Well productivity Operability Environmental effects Solids handling Well-design compatibility Flow assurance Reliability Gas handling Facilities requirements Power requirements Temperature limits Reservoir access Local knowledge/support New-technology risk Workover cost This paper focuses on the screening criteria related to a well’s hydraulic performance—namely, well productivity, gas-handling ability, and power requirements. Understanding what production rates are achievable with each lift technique in a particular environment, and knowing the interdependency these have with well count, well design, and facility capacities, is key to maximizing asset value. Equally important, quantifying how the free-gas volume entering artificial-lift equipment varies with drawdown and depth, and how this relates to the gas-handling capacity of the artificial-lift equipment, is necessary to ensure reliable operations without unduly constraining production. Finally, the power requirements have a direct effect on operational expenditures and, therefore, are important to quantify.

Reservoir to market: generating project value by coupling surface facilities with reservoir data in integrated asset modelling

Reservoir to market is a cutting-edge integrated asset modelling (IAM) initiative that is deployed to deliver robust field development and concept design solutions with enhanced project economics. The approach offers the potential to identify supply-chain cost reductions across the full spectrum of upstream developments, from full field development planning to individual equipment item modification. The process offers particular value in Greenfield and Brownfield development planning, in identifying and assessing sensitivity options and in definition of the optimum concept.

Integrated Asset Modeling: An Approach to Long-Term Production Planning

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 176322, “Samarang Integrated Operations: Integrated Asset Modeling— An Innovative Approach for Long-Term Production Planning Focused on Enhanced Oil Recovery,” by W. Sifuentes and J. Moreno, Schlumberger; P. Kumaran and R. Hamdan, Petronas; M. Muhamed Salim, M. Mohd. Som, H.W. Lee, A. Ahmad, N. Pangereyeva, S.S. Biniwale, N.J. Rodriguez, J.T. Nitura, R. Alia, O.A. Talabi, and V. Halabe, Schlumberger; and S. Pedraza, SPE, prepared for the 2015 SPE Asia Pacific Oil and Gas Conference and Exhibition, Bali, Indonesia, 20–22 October. The paper has not been peer reviewed. With declining trends in production and dwindling reserves for a 35-year-old offshore field, the Samarang Redevelopment Project was initiated with a vision toward implementing integrated operations as an asset-management decision-support tool. To realize expected reserves and optimize the production, an integrated-asset-model (IAM) concept is introduced with state-of-the-art modeling techniques to treat the asset as a whole unit rather than as isolated silos, which had been the traditional approach. Introduction The Samarang field is located in Subblock 6S-18 northwest of Labuan in eastern Baram Delta province. The production history of the field, first discovered and developed in the 1970s, is discussed in detail in the complete paper. A full-field review was carried out from 2004 to 2007 with the objective of identifying redevelopment opportunities. A field-development plan (FDP) was then established before an alliance was signed in 2010 to redevelop the field. The redevelopment team has drilled more than 17 infill wells, obtained new 3D- seismic data, carried out several studies including fine tuning of the enhanced-oil-recovery (EOR) development plan, and executed an extensive program of production enhancement and surveillance activities to boost the field production. This has given the team further insight into the Samarang field potential as well as the untapped near-field potential. The proposed EOR process is gas-assisted simultaneous water-and-gas (GASWAG) injection. This process consists of simultaneously injecting water updip in the gas cap and gas downdip in water-swept zones. The numerical reservoir-simulation model is the primary tool used for the EOR evaluations, optimizations, and forecasts. Implementation of the EOR scheme requires strategically positioning infill and injection wells. The recovery mechanisms targeted by this EOR scheme include Displacing attic oil back to existing well completions Sweeping the remaining oil toward new wells Repressuring the compartments Improving vertical sweep by using assistance gravity segregation Reducing residual oil in water by injecting gas in water-swept layers Traditionally, planning, forecasting, and optimization have been performed by the conventional approach of considering the reservoir, surface-network, and process-system models as being relatively isolated from one another. In this regard, many simplifications were taking place to translate the shared production limits, and account for surface backpressures, for each of the isolated models. These simplifications have negatively influenced production forecast for long-term predictions. The objective is to use an IAM approach to analyze and improve the production process for long-term planning.

Technology Focus: Reservoir Simulation (July 2015)

Technology Focus Coupled, full-field, integrated, holistic, or asset: Call it what you will, this class of modeling is becoming increasingly commonplace. The ability to interrogate the response of a system, from the reservoir to fiscal meter, is quite potent and encourages integration in all senses of the word—technically, operationally, and organizationally. Such end-to-end models may reveal bottlenecks, constraints, and potential flow-assurance issues. While the benefits are manifest, two related issues are worthy of consideration: complexity and piloting. The complexity of any individual component (such as the reservoir model) refers to the engineering judgment required to ensure that it is fit for purpose. For example, should one use a simple model (computationally rapid but approximate) or should a more complex model (computationally slower but accurate) be adopted? The former is better suited for rapid scenario screening and sensitivity analysis, while the latter is better suited for detailed engineering, with a spectrum of models in between. Piloting is another consideration. The degree of technical insight required to competently steer any single component simulator is often quite high. Having several simulators under a single, seamless platform places an extra burden on the pilots involved because the added layer of software (the integration platform) may mask possible shortcomings in a model, thus rendering potentially incorrect results and even suboptimal capital decisions. These considerations notwithstanding, I am genuinely optimistic about the potential these full-field models will bring and the insights they can deliver. More articles are certain to appear detailing predictions vs. observations and how planning, operations, and economics benefited from such integrated studies. I respectfully submit that the story of fully coupled, full-field, integrated asset modeling has only just begun. JPT

A Case Study of Integrated Asset Modeling for Gas Fields Sharing Production Facilities

This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 169918, ’A Case Study of Integrated Asset Modeling To Support Reservoir- Management Strategies for Four Dry-Gas Fields Sharing Production Facilities,’ by O. Espinola, SPE, M. Rocha, SPE, and N.C. Alvarez, SPE, Schlumberger, and J.R. Ramirez, A. Solis, and J.D. Guzman-Arevalo, Pemex, prepared for the 2014 Trinidad and Tobago Energy Resources Conference, Port of Spain, Trinidad and Tobago, 9-11 June. The paper has not been peer reviewed. Integrated asset modeling (IAM) properly models the interaction of fields producing through common production facilities and yields reliable production forecasts, compared with the use of independent models. Using integrated asset models helps validate independent models, honor facilities constraints, and, in general, support the decision-making process in reservoir management. This paper describes a case study in which four reservoir models were coupled with a production-network model, with the objectives of maximizing recovery factors, identifying operational problems, and evaluating water-production effects. Introduction Among the recent discoveries made by Pemex Veracruz Asset are four dry-gas fields with different geological and dynamic properties that must be produced through shared production facilities to be economic. This poses a complex operational scenario involving several questions that need to be answered: What is the optimum exploitation rhythm for each reservoir and its interaction on the surface? On the basis of the fact that one reservoir has an aquifer, what will be the effect on the recovery factor, and what are the challenges at the surface level? Regarding the differences in pressures and volumes, the selection of the compression stages will be difficult because each reservoir will require different capacity and startup dates. What, then, is the optimum compression scheme that generates the maximum value from an economic perspective? As part of the field-development plan, the incorporation of new wells or a new field will be evaluated. What will be the effect of these new elements on the system? To face all these challenges, a multidisciplinary team was appointed to analyze the system from different perspectives and design several exploitation strategies and evaluate them both technically and economically to select the optimum one to produce the fields. The strategies should Manage reservoir pressures to reduce backpressure effects. Minimize the risk to produce water and support the design of water-handling facilities. Identify the wells with liquid-loading problems and low potential. Define the startup date of each compression stage so that all reservoirs benefit at the same time. Evaluate the scenarios economically, considering two schemes—buying and renting compression facilities. Evaluate the effect of future developments, such as workover, infill drilling, and the incorporation of a new field.

Laggan-Tormore: reservoir to sales production forecasting and optimization using an integrated modelling approach

Abstract Laggan and Tormore are gas-condensate fields in the West of Shetland region of the UK, to be developed as a 143 km subsea tie-back (twin 18″ flowlines) to a new onshore gas plant (Shetland Gas Plant, SGP). Condensate will be sent to the neighbouring Sullom Voe Terminal for stabilization and a new 30″ gas export pipeline will provide a route from the SGP to Total's St Fergus terminal on the UK mainland. The development is planned as a hub to enable future development of similar reservoirs in the region. Production forecasting and field management are complex due to the evolving nature of the reservoir compositions over time, the back-pressure and flow assurance impacts of the long flowlines and final sales products being delivered at two remote terminals. This will be further complicated with the introduction of any additional fields, requiring an accurate understanding of field interactions, pipeline ullage, plateau extension and management of production allocation. To address these issues, Total E&P UK (TEPUK) have developed an integrated asset model (IAM) using state-of-the-art techniques to model the system from reservoir to final product sales. The IAM will be used to communicate a common geosciences and development understanding for forecasting and future development studies.