Real-Time Production Optimization Research Articles

SCADA Systems in Oil and Gas: Driving Innovation and Efficiency in the Digital Age

Abstract: In the oil and gas sector, supervisory control and data acquisition (SCADA) systems have become a revolutionary force that are transforming operations throughout the whole value chain. This in-depth analysis looks at the development, design, and various uses of SCADA systems in the upstream, middle, and downstream domains. It projects that the global SCADA market for oil and gas will grow to $4.52 billion by 2026. Real-time well monitoring, production optimization, and remote operations are made possible by SCADA systems in upstream operations, which greatly increase output and efficiency. The goal of midstream applications is to increase pipeline efficiency and safety by using predictive maintenance, enhanced leak detection, and flow optimization. By streamlining quality assurance, energy management, and process control, SCADA systems can completely transform refinery operations. SCADA deployments are not without difficulties, though, including data management complications, cybersecurity threats, and legacy system interoperability. In addition to addressing these problems, the study looks at new developments that could improve SCADA capabilities, such as edge computing, digital twins, AI and machine learning integration, and 5G technology. According to the research, SCADA is essential for promoting efficiency, safety, and innovation in the oil and gas industry. Businesses that successfully integrate SCADA technologies while overcoming implementation obstacles will be best positioned to prosper in the complicated and fiercely competitive energy market.

Robust production forecast and uncertainty quantification for waterflooding reservoir using hybrid recurrent auto-encoder and long short-term memory neural network

History-matching-based production forecast and uncertainty quantification are essential to achieve reliable risk assessment for waterflooding reservoir in the community of petroleum engineering, however, the conventional model-based history matching procedure presents intensive computation-cost due to numerous high-fidelity reservoir simulations. The use of direct forecast approach, e.g., data-space inversion (DSI) and its variants, is urgent to achieve direct production forecast without explicitly performing history matching. This work presents a generic data-driven post-history production forecasting framework using a novel deep recurrent neural network. We construct a hybrid recurrent neural network proxy through combining recurrent autoencoder with long short-term memory neural network, e.g., referred to as RAE-LSTM. The proposed RAE-LSTM takes history prediction and post-history well controls as input, while the post-history production responses (oil and water production, water injection rate) as output. Training samples are constructed by simulating high-fidelity models parameterized with various post-history well-controls and geomodels. Once the deep recurrent neural network proxies are trained offline, the online post-history production forecast and uncertainty quantification can be efficiently achieved by input user-specific well control sequences and history measurement. The proposed proxy model is demonstrated on two examples with varying complexity, e.g., a synthetic 2D Gaussian model and a 3D channelized EGG model. The use of our proposed RAE-LSTM neural network can be regarded as a nonlinear alternative to the DSI-type of linear statistical interpolation method. The comparisons between DSI and RAE-LSTM proxy confirm that the application of our proposed data-driven prediction method can effectively obtain more robust predictions and thus support rapid and reliable decision-making during the process of real-time waterflooding production management and optimization.

Evolutionary-assisted reinforcement learning for reservoir real-time production optimization under uncertainty

Production optimization has gained increasing attention from the smart oilfield community because it can increase economic benefits and oil recovery substantially. While existing methods could produce high-optimality results, they cannot be applied to real-time optimization for large-scale reservoirs due to high computational demands. In addition, most methods generally assume that the reservoir model is deterministic and ignore the uncertainty of the subsurface environment, making the obtained scheme unreliable for practical deployment. In this work, an efficient and robust method, namely evolutionary-assisted reinforcement learning (EARL), is proposed to achieve real-time production optimization under uncertainty. Specifically, the production optimization problem is modeled as a Markov decision process in which a reinforcement learning agent interacts with the reservoir simulator to train a control policy that maximizes the specified goals. To deal with the problems of brittle convergence properties and lack of efficient exploration strategies of reinforcement learning approaches, a population-based evolutionary algorithm is introduced to assist the training of agents, which provides diverse exploration experiences and promotes stability and robustness due to its inherent redundancy. Compared with prior methods that only optimize a solution for a particular scenario, the proposed approach trains a policy that can adapt to uncertain environments and make real-time decisions to cope with unknown changes. The trained policy, represented by a deep convolutional neural network, can adaptively adjust the well controls based on different reservoir states. Simulation results on two reservoir models show that the proposed approach not only outperforms the RL and EA methods in terms of optimization efficiency but also has strong robustness and real-time decision capacity.

Training effective deep reinforcement learning agents for real-time life-cycle production optimization

Life-cycle production optimization aims to obtain the optimal well control scheme at each time control step to maximize financial profit and hydrocarbon production. However, searching for the optimal policy under the limited number of simulation evaluations is a challenging task. In this paper, a novel production optimization method is presented, which maximizes the net present value (NPV) over the entire life-cycle and achieves real-time well control scheme adjustment. The proposed method models the life-cycle production optimization problem as a finite-horizon Markov decision process (MDP), where the well control scheme can be viewed as sequence decisions. Soft actor-critic, known as the state-of-the-art model-free deep reinforcement learning (DRL) algorithm, is subsequently utilized to train DRL agents that can solve the above MDP. The DRL agent strives to maximize long-term NPV rewards as well as the control scheme randomness by training a stochastic policy that maps reservoir states to well control variables and an action-value function that estimates the objective value of the current policy. Since the trained policy is an explicit function structure, the DRL agent can adjust the well control scheme in real-time under different reservoir states. Different from most existing methods that introduce task-specific sensitive parameters or construct complex supplementary structures, the DRL agent learns adaptively by executing goal-directed interactions with an uncertain reservoir environment and making use of accumulated well control experience, which is similar to the actual field well control mode. The key insight here is that the DRL method's ability to utilize gradients information (well-control experience) for higher sample efficiency. The simulation results based on two reservoir models indicate that compared to other optimization methods, the proposed method can attain higher NPV and access excellent performance in terms of oil displacement.

Study on Production Optimization Method of Fractured Reservoir Based on Connectivity Model

Due to the extensive development of fractures and serious heterogeneity in fractured reservoirs, it is difficult for the traditional numerical simulation method to invert its geology, which greatly limits the efficiency and accuracy of simulation and cannot realize the real-time optimization of production scheme. The connectivity model can only consider the two characteristic parameters of conductivity and connectivity volume, which does not involve complex and rigorous geological modeling. It can quickly and accurately reflect the state of the real reservoir, greatly reducing the simulation time, and is suitable for real-time production performance prediction of the reservoir. Due to the large difference in conductivity of fractured reservoirs, the difficulty of fitting increases. In this paper, the connectivity model is first applied to fractured reservoirs to realize the production dynamic simulation of fractured reservoirs. The optimization principle is used to optimize the injection-production scheme with the economic net present value as the objective function. In order to verify the method, the connectivity model is applied to Mu 30 of Changqing Oilfield in this paper. The results show that this method can effectively reflect the real production situation of the oilfield and the connectivity of the reservoir, and the simulation time is relatively fast. After optimization, the cumulative oil production of the reservoir increases by 8.1%, the cumulative water injection decreases by 2.3%, and the rising rate of water cut decreases by 58.8%, indicating that the connectivity model can realize the real-time production optimization of the reservoir.

Machine-Learning Techniques Assist Data-Driven Well-Performance Optimization

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 201696, “Robust Data-Driven Well-Performance Optimization Assisted by Machine-Learning Techniques for Natural-Flowing and Gas-Lift Wells in Abu Dhabi,” by Iman Al Selaiti, Carlos Mata, SPE, and Luigi Saputelli, SPE, ADNOC, et al., prepared for the 2020 SPE Annual Technical Conference and Exhibition, originally scheduled to be held in Denver, Colorado, 5–7 October. The paper has not been peer reviewed. Despite being proven to be a cost-effective surveillance initiative, remote monitoring is still not adopted in more than 60% of oil and gas fields around the world. Understanding the value of data through machine-learning (ML) techniques is the basis for establishing a robust surveillance strategy. In the complete paper, the authors develop a data-driven approach, enabled by artificial-intelligence methodologies including ML, to find an optimal operating envelope for gas-lift wells. Real-Time Well-Performance Optimization Wellsite Measurement and Control. - Flow Tests. - Past tests include sporadic measurement of multiphase rates and the associated flowing pressure and temperature, collected at various points of the production system, from bottomhole to separator conditions. Flow tests are also known as well tests; however, the authors use the term “flow test” in this paper to avoid confusion with well testing as used in pressure transient tests, including temporary shut-in pressure buildups (for producers) and pressure falloff tests (for injectors). Normally, a well would have limited data points from the past well tests (i.e., less than 50 valid flow tests in a period of 5–10 years). This data is the basis of creating ML models. Continuous Monitoring. - Every well should have adequate instrumentation, and its supporting infrastructure should include reliable power supply, minimum latency telemetry, and desktop access to production parameters. Alignment among real-time data and relational databases is required. Remote Control and Automated Actuation. - In addition to controllable valves, every well should be enabled with actuators to control the process variables. Remote control allows the operator to make changes to the current well-site configuration. Regulatory and Supervisory Control. - The value of automated closed-loop regulatory and supervisory control is to sustain optimal production while providing high well availability. Real-Time Production Optimization. - Continuous production optimization means that expected performance is challenged frequently by updating an optimal forecast with upper-level targets and current asset status. This is achieved by applying actions that close the gap between actual and expected performance. Faster surveillance loops compare actual vs. expected performance to determine minute, hourly, and daily gaps. A slower surveillance loop updates the asset’s expected performance. Well-Management Guidelines. - These are established, known limits to address and honor restrictions such as concession-contract obligations and legal limits, optimal reservoir management, flow assurance, economics, safety, and integrity.

Improved CRM Model for Inter-Well Connectivity Estimation and Production Optimization: Case Study for Karst Reservoirs

Due to the coexistence of multiple types of reservoir bodies and widely distributed aquifer support in karst carbonate reservoirs, it remains a great challenge to understand the reservoir flow dynamics based on traditional capacitance–resistance (CRM) models and Darcy’s percolation theory. To solve this issue, an improved injector–producer-pair-based CRM model coupling the effect of active aquifer support was first developed and combined with the newly-developed Stochastic Simplex Approximate Gradient (StoSAG) optimization algorithm for accurate inter-well connectivity estimation in a waterflood operation. The improved CRM–StoSAG workflow was further applied for real-time production optimization to find the optimal water injection rate at each control step by maximizing the net present value of production. The case study conducted for a typical karst reservoir indicated that the proposed workflow can provide good insight into complex multi-phase flow behaviors in karst carbonate reservoirs. Low connectivity coefficient and time delay constant most likely refer to active aquifer support through a high-permeable flow channel. Moreover, the injector–producer pair may be interconnected by complex fissure zones when both the connectivity coefficient and time delay constant are relatively large.

Real-Time Production Surveillance and Optimization in a Mature Subsea Asset

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 181103, “Real-Time Production Surveillance and Optimization at a Mature Subsea Asset,” by Xiang Ma, Zachary Borden, Paul Porto, Damian Burch, Nancy Huang, Paul Benkendorfer, Lynne Bouquet, Peng Xu, Cassandra Swanberg, Lynne Hoefer, Daniel F. Barber, and Tom C. Ryan, ExxonMobil, prepared for the 2016 SPE Intelligent Energy Conference and Exhibition, Aberdeen, 6–8 September. The paper has not been peer reviewed. A real-time production-surveillance and -optimization system has been developed to integrate available surveillance data with the objective of driving routine production optimization. The system aims to streamline data capture, automate data quality assurance, integrate high- and low-frequency data to extract maximum value, optimize the design and analysis of commingled well tests, and provide real-time multiphase well-rate estimates for continuous well-performance evaluation. Introduction The technology was piloted in an offshore field consisting of stacked deepwater channel deposits developed with five individual subsea drill centers, 15 active oil producers, one gas injector, and five water injectors. Equipment is controlled remotely, and produced fluids are routed to surface by two 8-in. risers from each drill center. Produced gas is treated for use as fuel gas on the surface and for gas lift by reinjecting into each production well. All wells were originally deployed with a full suite of instrumentation, as well as valve-status sensors for each well flowline and riser. The three-phase separators on the surface use orifice plate meters for rate measurement. The approach taken was to integrate real-time data and physical models for real-time production surveillance and optimization. The deployed system uses software that incorporates in-house-developed techniques for continuous model tuning. First, a complete integrated production model (IPM) of the production system was built, spanning reservoir inflow and wells on the one hand and flowlines, risers, and topsides on the other. The IPM is embedded in a field management software platform that houses several standard work flows as well as proprietary algorithms. Calculation results and real-time data are visualized with a visualization application. As with other digital-oilfield applications, the key is continuous calibration of the physical models when field producing conditions change. The standard solution within the chosen field- management software relies on calibrating the well models with single-producing-well tests. However, most of the subsea wells in the asset have lost the ability to flow alone, and only commingled well tests are available. Operators thus resort to a test-by-difference protocol to obtain periodic well rates for allocation, which introduces significant errors when test conditions deviate largely from production conditions. To overcome this issue, a customized commingled well-test-analysis (CWTA) work flow was developed to analyze multisegment commingled well tests simultaneously.

Technology Focus: Production Monitoring/Surveillance (March 2016)

Technology Focus Despite the market conditions during the last year, our industry continued to demonstrate further advances in the monitoring and surveillance field and to attest to the added value provided. General trends from past years are still being pursued to further our knowledge related to the issues seen as most important for production optimization and field management. Studies and methods were shared regarding unconventional development such as fracturing performance and analysis. New developments for multiphase-flow-metering evaluation and verification continue to be a leading topic. Additional studies were shared in understanding flow regimes in long horizontal wells using new methods and combined data gathering. Downhole solutions providing data in horizontal and difficult wellbore trajectories without intervention were further developed with tracers and other technology. New and novel methods for the use of fiber optics continue to come forth with advancements in strain and acoustic measurements that have been shown to add value. Challenges regarding the management of large data streams are still being discussed, although some interesting practical applications incorporating multiple data streams managed simultaneously in real-time scenarios were shared during the past year. Newer trends indicate further cooperation across the industry. Results from multiple collaboration efforts were released and discussed that involved partners from government agencies, operators, service providers, and various third parties working together to solve problems for the future. Case studies are now being presented for practical applications of advanced technology and optimization methodology that were considered only visions not that many years ago. It is very exciting for us to see the results from the efforts that have taken place over the past several years. Success cases for fieldwide implementation of real-time data gathering combined with work flows, modeling, and predictive analysis are becoming more prevalent. Subsurface monitoring applications relating to kick detection and managed-pressure drilling are gaining more attention as we also push the limits on the drilling side of the surveillance field. The technology initially developed for production monitoring may be forging the way for wide applications across oilfield operations. The following is a selection of articles to demonstrate the accomplishments shared by the exceptional peers who chose to publish their knowledge from the last year. Additional reading suggestions are also provided. JPT Recommended additional reading at OnePetro: www.onepetro.org. SPE 173452 Lessons Learned From Worldwide Deployments of Model-Based Real-Time Production-Optimization Solutions by Amol Bakshi, Chevron, et al. IPTC 17773 Real-Time Calculation of Fluid Level Using Dynamometer Card of Sucker-Rod-Pump Well by Yang Haitao, PetroChina, et al. SPE 172593 Combining Horizontal Production-Logging and Distributed- Temperature Interpretations To Diagnose Annular Flow in Slotted-Liner Completions by T. Ben Haoua, Schlumberger, et al.

Comparative study of feedback control policies in water flooding production

Smart well feedback control policies enable real-time production optimization by linking downhole measurements to valve control. This is more practical than model-based optimization methods that require an accurate prediction of reservoir models or fluid behaviors. This study numerically investigates reactive, proactive, and combined feedback control policies for a water flooding scenario. Long- and short-term economic optimization is compared for different well configuration schemes. Quantitative comparisons suggest that feedback control production improves recovery efficiency in water flooding compared with conventional well production for both short- and long-term optimization. A production policy that combines both reactive and proactive controls can achieve greater economic returns than simple reactive or proactive control alone. Isochronous feedback controls on both injection and production wells are superior to control on a production well only.

Results of Real-Time Production Optimization of a maturing North Sea Gas Asset with Production Constraints

Operating maturing assets poses increasingly complex challenges to operators. Meeting hourly or daily production targets becomes more difficult when wells are more often shut in for e.g. water washes (against salt deposition) or solvent jobs (at asphaltenes deposition). Declining reservoir pressure in turn results in less margin to compensate lost production while topside facilities can put constraints when production GOR and watercut increase at late production life. In a joint industry project of Wintershall, GdF, EBN, Siemens and TNO the applicability of real-time optimization is explored. The problem description, system architecture and initial optimizer results were presented in Linden (2013). This paper presents results of the optimization project and looks ahead to further field realization. The goal of this paper is to demonstrate that production of a mature North Sea gas asset can be optimized taking into account realistic constraints. All wells at this gas asset are intermittently shut in for water washes while daily nominations must be met. It will be shown that the optimization algorithm obeys the constraints and is fast enough for real-time application and thereby ready for field implementation.

Optimizing Frac-Pack Completions in Multilayer and Low-Stress-Contrast Zones

This article, written by Senior Technology Editor Dennis Denney, contains highlights of paper SPE 151870, ’Systematic Approach Used to Optimize Frac-Pack Completions in Multilayer and Low-Stress-Contrast Environment Encountered in the Usan Field,’ by M. Prada, SPE, Chevron; J. Hernandez, SPE, and R. Bouget, Total; and S. Green, SPE, Schlumberger, prepared for the 2012 SPE International Symposium and Exhibition on Formation Damage Control, Lafayette, Louisiana, 15-17 February. The paper has not been peer reviewed. The deepwater Usan field, approximately 100 km south of Port Harcourt, Nigeria, has multiple oil-bearing turbidite sands of varying thickness and permeabilities that are separated by massive and thin shale intervals, making the selection of completion intervals difficult. Fracture-height confinement is challenging because of the small stress contrast between the sand bodies and the bounding shales and the stress-field variability in a highly compartmentalized environment. Frac-pack completions were used in this field. The main challenges, lessons learned, and improvement opportunities captured during the first 2 years of the Usan completions campaign are presented. Introduction The Usan oil field is offshore Nigeria in Block OML 138, approximately 60 km south of the existing Amenan field (Fig. 1). Water depth ranges from 660 to 820 m. The Usan project is a stand-alone development and will comprise more than 40 subsea wells (oil producers and water/gas injectors) connected to a floating production, storage, and offloading (FPSO) facility. Oil producers are completed as cased-hole frac packs (CHFPs), and the water and gas injectors are openhole expandable-screen (OHES) completions. Some of the oil producers and gas injectors are equipped with surface-operated downhole flow-control valves to enable real-time reservoir management and production optimization. Reservoir Aspect These Usan Tertiary reservoirs have multiple turbidite-sand bodies of 3- to 30-m thickness with permeability up to 3,000 md and are separated by 3- to 20-m-thick shale layers, making them difficult to complete. In a smaller scale, some of the reservoir targets have very-thin (<5 cm) to medium (5–10 cm) laminated shale intercalations that are difficult to predict and exclude from the completion interval. Because of the strong compartmentalization, there are important variations of fluid parameters between reservoir levels and panels. Oil density ranges from 20 to 40°API, and in-situ viscosity ranges from 0.2 to 4 cp. Oil is slightly undersaturated, with an average reservoir pressure 10 to 20 bar above saturation pressure.

Intelligent-Field Management: Monitoring and Optimization of the Greater Ekofisk Area

This article, written by Senior Technology Editor Dennis Denney, contains highlights of paper SPE 150424, ’Intelligent-Field Management: Real-Time Monitoring and Proactive Optimization of the Greater Ekofisk Area,’ by Amit Madahar and Alannah McIntosh, Weatherford Production Optimization; Ilnur Mustafin, ConocoPhillips; and Nick McAlonan, SPE, Weatherford Production Optimization, prepared for the 2012 SPE Intelligent Energy International, Utrecht, The Netherlands, 27-29 March. The paper has not been peer reviewed. An online real-time production-optimization and monitoring system for the greater Ekofisk area (GEA) of Norway was installed in 2006. The system has evolved significantly since installation, with changes driven by multidisciplinary teams in coordination with the operator’s Production Optimization Centre (POC). This online system integrates data from the complete production system—reservoir to export meters. The system enables real-time monitoring of all wells and their associated instrumentation parameters along with field three-phase production allocated to the well level. The system alerts engineers when a well’s performance is outside predefined tolerances, thereby enabling continuous optimization of the combined field network. Introduction The GEA has a high level of activity in terms of well intervention and drilling programs. Therefore, communication about the current operation status across a large multifunctional organization is essential. The GEA has only three fiscal-metering points to measure production. Although sufficient for production allocation at the field level, it results in uncertainty at the platform level. This situation could lead to lost production-enhancement and optimization opportunities. The POC, subsurface-production-delivery, and reservoir-optimization teams are tasked with assessing real-time data from, nominally, 160 active producers and injectors to minimize losses and maximize field production. An important function of the asset team is to provide daily reports for wells and topside losses. It usually is difficult to compile this information accurately in a short time frame. Most GEA production wells use gas lift; therefore, it is important to use the finite lift gas efficiently. This optimization process uses an asset-network model. If the asset-network model is not updated frequently and maintained, it will lose its ability to predict well-production rates and pressure drops in the system accurately. To resolve these problems, the asset uses a multidisciplinary tool called intelligent daily operations. It is used as a real-time-monitoring tool within the POC and is used for daily well-performance monitoring by the production-delivery engineers. This tool facilitates daily well-production variance to detect under-performing wells and flag production-optimization and well-service opportunities. It provides automatic updates to the network model. The real-time-monitoring tool implements various workflows to enable the different departments to collaborate and share the most-up-to-date information and removes the large amount of manual effort in gathering and analyzing the data, thus allowing engineers to concentrate on increasing production and minimizing losses.

Real-time production optimization and reservoir management at the IO center

This paper presents research results within real-time production optimization and reservoir management at the Center for Integrated Operations in the Petroleum Industry (IO center). This includes life-cycle and long term issues like well location and target production rates. Further, results on short term production optimization, for instance the allocation of target production between wells and routing of wells into pipelines, will be presented. Finally, results on value chain optimization, where the production chain from reservoir to export is modelled, are reviewed.

Optimization of petroleum production networks – Through proxy models and structural constraints

This paper proposes and explores the properties of a solution algorithm designed to solve network simulation problems to optimality. Emphasis is put on the system being divisible into components, as this underlying assumption motivates the algorithm in its entirety in that rather simple relations between the system components are modeled as explicit structural constraints, while the significantly more complex relations within each component are approximated based on the underlying simulator data. The Real-Time petroleum Production Optimization (RTPO) problem is used as an example of such a network simulation problem that can benefit from using the solution approach outlined. The paper also includes the mathematical formulation of the RTPO, followed by a description of the algorithm and a comprehensive study of the algorithm's properties on a real RTPO application. Finally, the results are compared to a reference case and conclusions are reached about the method.

Lagrangian Decomposition of Oil-Production Optimization Applied to the Troll West Oil Rim

Summary This paper proposes a method based on Lagrangian decomposition for real-time production optimization. It argues the method is well suited for the well-allocation and routing problem in the upstream industries. A semirealistic field case assuming steady-state conditions is used to compare the merits of the method with current industrial practice. The method compares favorably and is therefore an interesting option in real-time production optimization. There are several reasons for this. An error bound on the solution of the production optimization problem can be computed, which is clearly information of interest to any user. Furthermore, the algorithm is efficient and can be parallized for an even higher efficiency. Finally, the Lagrangian decomposition method provides a tool for understanding how a limited resource, such as gas capacity, is used in production optimization.

Wireless sensing on surface hydrocarbon production systems

The use of wireless sensor networks for monitoring and optimising the performance of surface hydrocarbon production systems is reported. Wireless sensor networks are shown to be able to produce comprehensively instrumented XTs and other equipment that generate the data required by Intelligent Oilfield systems. The information produced by such systems information can be used for real-time operational control, production optimization and troubleshooting.

Handling Real-Life Well-Production Instabilities and Uncertainties in Digital Fields: A Practical Application From Congo and Gabon

This article, written by Senior Technology Editor Dennis Denney, contains highlights of paper IPTC 12545, "How To Handle Real-Life Well-Production Instabilities and Uncertainties Within Digital Fields: A Practical Application From Congo and Gabon," by Jacques Danquigny, SPE, Marc Tison, Guennole Ouaye, Emmanuel Segui, and Michel Vie, SPE, Total, prepared for the 2008 International Petroleum Technology Conference, Kuala Lumpur, 3-5 December. The paper has not been peer reviewed. Digital fields use modeling tools to optimize production. Modeling is challenged by transient and instable flow regimes. Optimization requires a further level of accuracy: It implies the modeling of an envelope of production points. This study highlights parameters having the main influence on well-model outputs. It shows the accuracy of these models that can be used to carry out virtual flow metering. It also provides guidelines to keep well-flow models updated, which is key to sustaining real-time production optimization. Introduction To address the production-optimization challenge on a daily or even real-time basis, many oil companies are developing digital-fields technology. Total has developed a fit-for-purpose tool, which was installed as a pilot in a mature field in the Congo. This performance-modeling tool provides data visualization that is used as an alarm system. One workflow is automatic modeling of production tests. The modeling tool automatically detects new production tests in the corporate production-database-management system (PDMS). Downloading the averaged results of tests from PDMS, along with the reservoir static pressure and the productivity index (PI) of the well from the reservoir database, the test is modeled with two computation techniques, system calculation and quick-look. Studied Set of Production Tests This study considered production tests of gas lifted wells in a mature Congo field between January 2007 and February 2008. This period was long enough to obtain a significant number of tests per well and short enough that static pressure and PI remained relatively constant for each well. Proper instrumentation and monitoring were required for all real-time inputs and outputs of the PI application. For all the statistical computations, these PI tags were resampled with a constant timestep of 120 seconds. Some of the tests were not valid because operational events made them unrepresentative. Therefore, these tests were not considered in the study. On average, five tests were studied per well, with a total of 254 tests analyzed. Typically, test duration was 12 hours. Flow-Production Regimes and Production-Parameter Variability Among the 254 tests, some showed fairly constant conditions whereas others were unstable. It was expected that the unstable features affected the quality of the modeling. Dealing with unstable wells required more-accurate definition of the different flow/production regimes observed in gas lifted oil wells. An unstable periodic-production regime does not necessarily have a sinusoidal trend, and transient tests can undergo a change in both signature and trend. Once the flow/production-regime map was established, each production test of the survey was classified according to this map. Although this mapping was somewhat subjective, numerical criteria were defined to classify each test, as detailed in the full-length paper.

Holistic Production Optimization Achieved One Workflow at a Time

Technology Update Sustained production optimization, a longstanding objective within the exploration and production sector, has witnessed only incremental and sporadic advances. Obstacles to adoption of a holistic approach seem intransigent—an apparently daunting challenge to individual stakeholder groups. Yet, within the industry, there is a renewed sense of drive toward consistency, and growing consensus, on key components. Effective production-data management, model management, and real-time production-optimization systems are necessary but not sufficient. Integration of these components by means of orchestrating critical workflows—to provide a human/system interface for efficient organizational, operational, and technological collaboration—is required. To overcome inertia requires a reductionist approach to the problem. As the adage goes, we eat the elephant one bite at a time. Workflow orchestration within an overall scheme for integrated production operations is a pragmatic implementation approach, one now being applied successfully to a diverse range of projects. Production as a System of Interrelated Workflows Operational-excellence initiatives have improved individual workflows to reflect best practices. However, because of the degree of fragmentation inherent in many assets, these initiatives often remain inefficient. Cultural and operational influences significantly determine the degree to which an organization is either experience-driven or process-driven. The orchestration of technical workflows can play an important role in creating a system capable of meeting the necessary demands by means of capturing implicit expertise and automating many processes to a significant degree. Consider the components of a workflow in which data are analyzed and interpreted against some context (e.g., a model) antecedent to decision making. The degree of discontinuity experienced in a workflow cycle and its connectivity to the context of the entire production system determine the effectiveness and efficiency of the workflow. Thus, integrating these components and orchestrating the workflow cycle across an organization are necessary for improved optimization. The accurate definition of workflows and their interaction can be achieved by means of decomposing the main value-chain activities. Improving decline analysis or monthly economics forecasting may be of value; however, greater value is realized by combining these tasks into a prediction of the economic impact of a new decline curve. A holistic effort must be guided within the context of the system in a synchronized way that reflects the interrelatedness of the workflows. The next step change involves orchestration of work processes and their technology interfaces. Workflows reflect the needs of a particular basin, well, facility, operation, and organization, and workflows accurately reflect the entire value chain from reservoir to sales meter. Removal of workflow bottlenecks across the value chain improves the asset's overall effectiveness, thus adding value. Spanning discipline silos, systematizing knowledge capture, providing timely data to stakeholders, and streamlining the numerous human/system interfaces involved in a workflow improve cycle times and the quality of decisions, as well as minimizing the need for change management. Perhaps most significantly, orchestrated workflows leverage the expert human resources available to the asset. This distributed workflow system addresses the specific needs and challenges of operational and technical workflows.

Real-Time Production Optimization of Oil and Gas Production Systems: A Technology Survey

Summary This is a noncritical survey of key literature in the field of real-time production optimization of oil and gas production. The information flow used for optimization of the system is described. The elements in this description include data acquisition, data storage, processing facility model updating, well model updating, reservoir model updating, production planning, reservoir planning, and strategic planning. Methods for well prioritization, gas lift optimization, gas or water injection optimization, and model updating are discussed in the view of the information flow described. Challenges of real-time production optimization are also discussed. Introduction In the daily operation of an oil and gas production system, many decisions (an element of a solution) have to be taken affecting the volumes produced and the cost of production. These decisions are taken at different levels in the organization, but eventually they will reach the physical production system. Fig. 1 gives an overview of a physical production system. For such production systems, the decisions are related to the choke or valve openings, compressor, and pump settings at every instance of time. An objective function is a single-valued and well-defined mathematical function mapping the values of the decision variables into a performance measure. Examples of such performance measures are the total oil production rate, net present value (profit), or the recovery of the reservoir. In the efforts toward better performance of the production system, a question to be answered is which decisions are better to maximize or minimize the objective function. In the process of making good decisions, information about the production system is used. This information may include the physical properties such as pipe diameters and lengths, or it may include measurements from the production system. The environment in which the production of oil and gas is obtained is constantly changing. This will affect the value of the performance measure of the decisions used. For example, if the cooling capacity of the production system is an operational bottleneck, this may no longer be the case if the seawater temperature drops or another pump in the cooling system is started. Incidents in the production system may also affect the value of the performance measure of the decisions. A partial shutdown of the production system because of maintenance will most likely also affect system bottlenecks. Real-time optimization (RTO) is a method for complete or partial automation of the process for making good or optimal decisions. The term "optimal" is defined below. By continuously collecting and analyzing data from the production system, optimal decisions may be found. Either these settings are then implemented directly in the production system or they are presented to an operator or engineer for consideration. If the settings are implemented directly, the RTO is said to be in a closed loop. RTO defined by Saputelli et al. (2003a) reads: "a process of measure-calculate-control cycles at a frequency, which maintains the system's optimal operating conditions within the time-constant constraints of the system". The main aim of RTO is to improve the utilization of the capacity of a production system to obtain higher throughput or net present value. The idea is to operate the production system, at every instant of time, as near to the desired optimum as possible (Sequeira et al. 2002). To achieve this, a model of the production system is optimized to furnish an optimal solution. The model is continuously being updated by measurements from the production system to fit the actual input-output behavior of the processing facilities, wells or network, and reservoir better.