Multiphase flow metering is an important tool for production monitoring and optimization. Although there are many technologies available on the market, the existing multiphase meters are only accurate to a certain extend and generally are expensive to purchase and maintain. Virtual flow metering (VFM) is a low-cost alternative to conventional production monitoring tools, which relies on mathematical modelling rather than the use of hardware instrumentation. Supported by the availability of the data from different sensors and production history, the development of different virtual flow metering systems has become a focal point for many companies. This paper discusses the importance of flow modelling for virtual flow metering. In addition, main data-driven algorithms are introduced for the analysis of several dynamic production data sets. Artificial Neural Networks (ANN) together with advanced machine learning methods such as GRU and XGBoost have been considered as possible candidates for virtual flow metering. The obtained results indicate that the machine learning algorithms estimate oil, gas and water rates with acceptable accuracy. The feasibility of the data-driven virtual metering approach for continuous production monitoring purposes has been demonstrated via a series of simulation-based cases. Amongst the used algorithms the deep learning methods provided the most accurate results combined with reasonable time for model training.

Oil wells in Western Siberia usually placed on artificial drilling pads, forming well clusters up to 30 wells. The flow rate of each well in the cluster measured by an automatic measuring unit one by one. Often flow rate measurement requires several hours and flow rate of a single well can be measured once a week or less. This led to situation then events affecting well rate can be invisible between measurements. Identifying such events can be extremely useful in many cases, for example for wells with unstable behavior or transient regimes. The same challenges are also faced at distant green fields during their development, there the flow rates can be measured once a month with a mobile unit. The objective of this paper is to develop a virtual flowmeter model based on indirect high-frequency data of well operation and ESP. In Gubkin University, at the Petroleum Reservoir and Production Engineering Department, bench tests of ESP5-50 (118 radial stages) on gas-liquid mixture in a wide range of volumetric gas content ( β in = 0-60%), intake pressure ( P in = 0.6-2.1 MPa) and pump shaft speed ( n = 2400-3600 rpm) were performed. Three vibration sensors were installed on the unit: on the ESP, at the ESP discharge, on the pipeline, which simulates the wellhead production tree. During the bench tests were recorded series of pressures at the intake, discharge and along the pump length, series of current and power consumption, as well as vibrations with frequency several times per second. Based on the bench test results, we investigated the possibility of indirect determination of well operation parameters during artificial lift modelling by machine learning. As a result, the approaches to modelling taking into account various sets of parameters (features) have been studied: based on hydraulic parameters – ESP intake and outlet pressure; based on hydraulic and electric parameters – current and power consumption; based on hydraulic, electric and vibrating parameters. The analysis of data series allowed to define the boundaries of stable ESP operation, namely the transition to surging and pump starvation. The novelty of the work is: – machine learning modeling of the gas-liquid mixture pumping process by electric submersible pump; – solving both direct and inverse issues: as virtual liquid flowmeter as, virtual gas content flowmeter at the pump intake.

Hydrocarbon allocation becomes essential when facilities are shared for various contributing wells or reservoirs. Uncertainty in the reassignment factor can significantly impact operators’ daily decisions, reservoir health, and ultimately the organization's financial investment. Historically, most oil companies rely on the latest production test done at the actual choke to calculate the produced volume per string, regardless of changes in well performance over time. But this simplistic routine leaves a gap between estimated and actual rates that require more user interventions for manual inputs or corrections. This paper illustrates an example of hydrocarbon allocation process from a smart field. An intelligent optimization system that receives real time data instantly automatically back allocates the field rate based on continuously-tuned well performance curves at wellhead node. This system is capable of managing large amount of data as well as combining analytical models, data mining with predictive analytics, bringing significant benefits to reservoir management, and surveillance process. However, it was found that the field correction factor fluctuated about 25%, when some production optimization and well operations were performed due to inconsistencies in well performance curves. The traditional methodology to calibrate well models starts by choosing the nearest multiphase flow vertical correlation to the measured pressure gradient, reducing the error between calculated and measured points by tuning the "L factor." This procedure cannot ensure that the resulting model will satisfy other production tests at different flowing conditions, which reveals the difference between accuracy and consistency. In contrast, the present effort suggests a practical approach for better estimation of daily well rates as well as improving allocation process, since it attempts to minimize the error between representative production tests and well performance curve rather than reproduce a high accurate bottom hole pressure for a particular flowing condition. Additionally, a more representative productivity index is derived from this change. This novel approach showed better well rate estimation for different flowing conditions reducing reassignment factor deviation below 5%. Consequently, it proved that having a more consistent well model tuned by representative production tests and controlled by real time data, enhances oil back allocation process.

Significant fluid property variation can be induced due to pressure and temperature dynamics in the reservoir associated with oil production. The existing analytical solutions for temperature transient analysis generally assume constant fluid properties, which can be invalid especially for cases of high drawdown and strong temperature signals. In this study, we present a method to account for the fluid property variations in temperature transient analysis. The method introduces corrections on fluid property values as input for analytical solutions. The corrections are performed on four identified fluid properties in an iterative manner which can be easily implemented in available temperature analysis procedures. To validate the developed approach, we model drawdown and buildup transient temperature signals with the fluid property correction method for non-damaged and damaged reservoirs under different production rates and reservoir fluid compositions. The analytical modeling results are compared with numerical simulations. In addition, by finding the dominating fluid property, a simplified approach of property correction is presented. Through application to example problems, we show that using fluid property correction method presented herein can improve the permeability estimations by 60% for the conditions considered in this paper. We present a modified method to damaged reservoirs which results in additional 25% improvements on the permeability estimations. With these improvements, the applicability of temperature transient analysis using analytical solutions can be extended from cases with limited sandface temperature signals of a few degC to stronger signals of 20-30 degC.

Asphaltene agglomeration and deposition is a complex process, leading to progressive production losses without mitigative actions. Injection of asphaltenes inhibitors (solvents) in the tubing is a common practice to avoid deposition in production tubing. This does however not avoid deposition in the near-wellbore. Wells suffering from asphaltenes depositions are regularly shut in for ‘solvent jobs’, injection of chemicals to dissolve asphaltenes in the (near-)wellbore. After a solvent job the well performance slowly degrades again due to new deposition. The goal of this study was to optimize the production-solvent cycle in terms of maximizing production profit, by means of a data-driven model capturing both well and reservoir performance. TNO and ENI have developed a grey-box model to capture the impact of asphaltenes deposition in the near-wellbore on oil production. This effective PI-model is calibrated to actual field data and has shown to predict the effect of deposition and the resulting production decline well. This paper describes the background and modelling approach for both the grey-box reservoir deposition as well as the wellbore and choke pressure drop – predicting near-future oil prediction based on current performance. Examples of calibrating the grey-box deposition parameters are given and for the used well data the prediction window shows to be consistent for at least 20 days ahead. The outcome of the developed functionalities is an estimated ‘optimal cycle length’ and therefore a suggested next date of performing a solvent job at a specific well. The methodology was developed using production data from a well equipped with multiphase flow meter (MPFM), but is applicable to wells with intermittent flow readings as well. The methodology can handle any realistic trend in production fluid (GOR, WC) as well as operator input (choke, FLP back pressure) and economic constraint (OPEX, solvent job cost relation). The developed functionalities are applicable for real-time implementation, even without optimization of the code in terms of computational speed. Current production data is available on an hourly basis, wheras the calculation of the optimal solvent job cycle (including predicting oil production rates one month ahead) takes a few minutes maximum. A demonstrator implementation in OVS software can be used to optimize solvent job cycles on individual well basis. The operator can through choke variations manipulate the predicted date of the next optimal solvent job when scheduling solvent jobs at multiple wells (under equipment constraints). Extension of this work can include optimization of an asset of wells, maximizing total production under practical solvent job constraints.

Rubiales and Quifa fields are major heavy oilfields (oil gravity ranges from 11.3 to 14.4 °API) in Colombia with a current oil production of more than 264 MSTB and an oil viscosity range from 370 to 730 cP. Horizontal well technology used to drill through unconsolidated sandstones with an active and strong aquifer, under primary depletion, has been used to develop this field. Since 2006, 1,019 horizontal producer wells have been completed using slotted liners (conventional completion) because of the low cost and effectiveness in controlling the coarse-grained sand formation and low fines production. The high water production rate from the beginning of the operation in the horizontal wells is the primary problem in these fields because of the high cost of produced water treatment and other factors. In formations in which high permeability, high oil viscosity, and strong aquifers are combined, early water influx can result. Water production is predictably associated with the oil production; however, one of the major challenges is to delay the water production for as long as possible. Currently, a large number of wells are closed in that have reached economic limits, primarily caused by high water cuts. An autonomous inflow control device (AICD) was placed in each screen joint to balance the production influx profile across the entire lateral length and to compensate for the permeability variation and therefore the productivity of each zone. In late 2012, a new technology project was designed and implemented as a pilot to help address the described issues in horizontal wells. AICDs would be used to maintain equilibrium of produced fluids along the horizontal section of the well, ultimately delaying water coning. After the first pilot is completed, a technical analysis could be conducted, based on the results obtained, and, if possible, the AICD technology could be implemented in additional prospect wells. The AICD pilot was implemented in geological conditions more adverse than wells previously completed with conventional completions and still other wells previously completed with passive but non-autonomous inflow control device (ICD) completions; the pilot will be evaluated with horizontal wells of the area. The main purpose of this paper is to provide details about the selection process, design, and evaluation results for the use of the AICDs in horizontal wells in both heavy oil fields.

Smart Metering Solutions have been developed and implemented on TOTAL assets by combining digital technology and in house metering expertise to improve quality of data needed for reservoir monitoring, installation operation and fiscal applications. Deployment within TOTAL upstream operations is presented with specific highlights both on the design of metering systems and network but also on the real operation of metering and allocation systems in the field. TOTAL Smart Metering Solutions used so far to improve field operation of systems are based on Optimized metering network design with selection of suitable metering and communication technology. Remote Metering Monitoring (RMM) which consists basically in the continuous monitoring of pertinent flow and sensor process data which are made available in real time historian data base. Use of customized software tools ranging from business routines for routine follow up of metering systems and sensors to advanced software for overall verification and validation of like data validation and reconciliation (DVR) Collaborative work in Smart Metering Room (SMR) putting together experts, site specialists, data users and suppliers to quickly trouble shoot systems Examples of Remote Metering Monitoring and Smart Metering Room applications used by TOTAL for surveillance of multiphase metering systems located in different units worldwide with limited access are presented. In addition to significant improvement on metering performance and data quality, implementation of Smart Metering is associated with cost reduction and more efficient way of working / solving problems.

Empirical equations have always been used to fit and later predict oil rate production. The most common and widely use empirical equation are those we use during production decline analysis. With decline analysis equations we are able to predict in most cases production decline as a consequence of reservoir pressure decline. However, these equations will not perform as desired when a sudden reduction on the oil rate of a well has occurred as a consequence of changes on the well head parameters such as a change of the production choke or the gas oil ratio (GOR). These unexpected changes of the oil rate are not able to be predicted with most common used decline analysis equations and generally speaking with deterministic equation sets. In this paper, a new empirical equation is described to allow the calculation of oil rate from a given well when only information of choke size and well head pressure is available. Calculated production from this equation shows outstanding result in terms of precision when compared with other equations available in the literature like Gilbert, W.E. (1954) and Ros, N.C.J. (1961) . In addition, this equation can serve as a great operational surveillance tool when incorporated into a centralized production system and database. Finally, the methodology is described in a way that the reader can apply and adjust the equation to be used in oil fields with naturally flowing wells and stable water cuts.

Saudi Aramco's rapidly growing portfolio of fields and wells demands the employment of innovative approaches to manage its assets efficiently in today's challenging economy. One of the main strategies that the company has adopted is the creation of a highly interactive surveillance environment called Production Operations Surveillance Hub (POSH), that supports business collaboration and efficient decision making processes. POSH enables the business to exceed geography and time by consolidating multidisciplinary team knowledge, streamlining communication, providing real-time collaboration and facilitating quick decision making. The POSH also provides an e-workplace for oil and gas production automation and optimization that allows business team members and their partners (within and outside the company) to communicate and coordinate activities from anywhere in real time. The creation of this environment started with a comprehensive assessment of the production engineering (PE) activities and processes. The assessment was conducted through electronic surveys, face-to-face interviews and interactive workshops to gather the required information from PE experts and develop a full understanding of their work processes. These “as is” processes were then transformed into “to be” workflows where technology applications were associated with each step of the process. A detailed design phase followed, which involved defining the engineering calculations, the communication protocol and the data exchange pathways among the different components of each workflow and between the workflows. A cataloging and organization phase followed in which four main themes were identified for the POSH and aligned with Saudi Aramco's intelligent fields' development strategy. The four focus areas include: well and field activities management; intelligent field equipment performance and real-time data health; electric submersible pumps (ESP) surveillance and pre-diagnostic; and well production and performance monitoring. This paper describes the assessment planning and implementation process. The paper also illustrates the value of this process through elaboration on the assessment outcome and provision of details of the POSH themes and functions. This step is essential for the success of the deployment of Saudi Aramco's first POSH and similar collaborative environments.

Extended Reach Drilling (ERD) wells can be extremely long and technically challenging from drilling through completions, clean up, stimulation and production optimization. This paper will focus on the key challenges associated with ERD well completion, real-time clean up, production monitoring and optimization and will present an advanced intelligent solution enabled by new technologies to handle these complex challenges. As the fields become more mature, there is an increasing requirement for real time flow profile understanding and interventions to correct unwanted flow profile anomalies such as water production, gas breakthrough, etc. to have better production and reservoir management. By combining real-time data monitoring and active control with optimal zonal isolation along the wellbore has been proven to be one of the most efficient and effective completion strategies in the industry for the abovementioned requirements. The operator can proactively manage production, eliminate or reduce interventions and associated production downtime, well costs and risks. However, getting the invention less permanent intelligent completions to the Total Depth (TD) of ERD wells is one of the major challenges and hence very long horizontal ERD wells are typically completed partially leaving out stretches of reservoir section unmonitored and uncontrolled. Also, conventional intelligent completions have clear limitations on number of flow control valves and sensors that can be deployed in a well, mainly limited by the maximum number of control lines, which can be deployed with the completion. The limitation comes from the feedthrough port limitation of critical completion tools such as tubing hanger, production packer etc. and also from the completion deployment challenges faced with increasing number of control lines. This limits the ability to have an optimal segmentation along the wellbore in many cases. Another important challenge that our industry faces is in effectively using real-time monitoring data to make informed decisions and take required actions efficiently. Traditionally the whole process of data gathering from down hole tools to decision making and taking reasonable action is long ranging from months to years and requires multi-disciplinary expertise. As a result the well controllability, reservoir management and hence production optimization are compromised due to technology solutions limitation. New all electrical solution with reliable downhole wet connects and integrated answer product software for data management and analysis of real-time data presented here is a key intelligent end to end solution. This paper gives details of this new generation technology solution that enables above mentioned challenges.

There is a critical need for robust and reliable downhole power generation and storage technologies in order to push the boundaries of downhole sensing and control. Downhole power harvesting is an enabling technology for a wide range of future production systems and applications including self-powered downhole monitoring, downhole robotics, and wireless intelligent completions. This paper provides an analysis of available ambient energy sources in the downhole environment, and various energy harvesting techniques that can be employed to provide a reliable solution. Advantages and limitations of conventional technique like turbine are compared to advanced energy harvesting technologies. The power requirements and technical challenges related to different downhole applications have also been addressed. We have also presented a single-phase flow energy harvesting system based on vortex-induced vibrations by employing a bluff body in fluid flow. A laboratory experimental setup was developed that consists of a 2” flow loop, piezoelectric energy harvester, power management module, and capacitor storage. Computational fluid dynamics (CFD) simulations of the vortex-induced flow-vibrations for our setup have been presented. The generated vortex shedding frequencies and generated power in the flow loop is very low, which suggests requirement of a reliable rechargeable battery. Finally, recommendations are made for improvement and achieving the goal of practical implementation of downhole power harvesting.

Flow-rate testing is the most fundamental and essential form of reservoir surveillance and is typically performed on a monthly basis using a test separator. This case study reviews the results from modelling a well located in a jungle for which poor testing quality precluded inflow analysis. The main causes were logistical difficulties, large flow transients, and phase segregation in the flowline between the wellhead and test separator. A virtual flowmetering technique was investigated to determine a trend of reservoir pressure and skin and identify production optimisation opportunities. The algorithms used real-time data from permanent downhole gauges and electric submersible pump (ESP) surface controllers, which provided the necessary measurement frequency, resolution, and repeatability to capture well performance transients and provide a full production history. The liquid rate calculation used the principle that the power absorbed by the pump is equal to that generated by the motor. Water cut was calculated by modelling the production tubing pressure drop with a multiphase correlation. Virtual flowmetering is demonstrated to provide a credible alternative where test separators and multiphase meters cannot be deployed owing to logistical difficulties such as in swampy terrain or on unmanned platforms and subsea. Furthermore, the high frequency, resolution, and repeatability of this particular flowmetering technique are greater than those obtained with traditional monthly, or even weekly, test separator data. This case study illustrates how these three attributes enhance nodal analysis, pressure transient analysis (PTA), and superposition, which enabled monitoring the well's reservoir pressure and well interference without the need to conduct pressure buildups. Additional value comes from extending the application of this virtual flowmetering technique to wells that do not have testing difficulties, especially those that are under producing and require detailed diagnostics (e.g., PTA). The technique is also well suited to application in true real time because it is analytical, valid across the full pump range, and able to capture transients without requiring an iterative process. Is real-time flow rate just around the corner?

Horizontal well is an important technology to enhance oil recovery for both old oilfield and new oilfield. Horizontal well have four characteristics: large drainable area; small producing pressure drop; high fluid productivity index and long water free production period ( Joshi S. D. 1991 ) compared with vertical well. The contact pattern between horizontal section and the layer is line contact, and the pressure drop around the horizontal section is a linear distribution. The pressure drop near the bottom of the horizontal well will be far less than that of the vertical well if the same fluid is produced. Therefore, horizontal well can inhibit bottom water coning more effectively. The critical production of horizontal well is greater than that of vertical well for bottom water reservoir(Chaperon 1986, Papatzacos P. et al. 1991 ). But horizontal well may form water cresting. The reserves controlled by the horizontal section and volume swept by the bottom water is far larger than that of the vertical well, so it can increase the cumulative water free production. Therefore, comparing with vertical well, horizontal well can prolong water breakthrough time and improve the ultimate recovery (Permadi P et al. 1995, Wibowo W et al. 2004 ), which is one of the reasons for horizontal well technology used in bottom water reservoir widely at home and abroad. But with the oilfield development, horizontal well has also exposed some questions gradually. Vertical well has a long water cut production period, but water cut of the horizontal well increase dramatically after water breakthrough, which affected horizontal well development seriously (Souza A L S. 1997).

Almost a decade has passed since Saudi Aramco implemented its first intelligent completion in a remote area field. Nowadays, the field is the crown jewel of intelligent well completions worldwide, with a large number of inflow control valves (ICVs), a permanent downhole monitoring system (PDHMS), and multiphase flow meters (MPFMs). This paper summarizes the lessons learned from the assessment and optimization of hundreds of laterals in the field. It also shows how proper ICV utilization leads to production optimization throughout the field. It sheds light on the best practices that helped to realize the full potential of those installations. Finally, the paper ends with recommendations for the way forward and conclusions. Examples will be discussed in detail to show clearly the benefits and results that could be harvested from the use of intelligent completions. These examples will show some of the benefits that include the revival of dead laterals, controlling undesired fluid breakthrough, and managing sweep efficiency.

Proven to maximize hydrocarbon production or well injectivity, horizontal multilateral completions have witnessed growth both in number and complexity over the years. These well types brought challenges with respect to well accessibility for rigless well intervention, and the ability to access these wells has been developed using coiled tubing (CT) to enable operations, such as reservoir stimulation, to be carried out. One of the main challenges is to identify and access each lateral, to be able to perform the stimulation to maximize well production. This challenge has been encountered in Saudi Arabian multilateral wells, which are drilled in a carbonate reservoir with an open hole (OH) completion and where the laterals are typically left with a high formation damage (skin effect) after drilling, thus requiring stimulation to improve their performance. At present there is no single technique for lateral identification without tagging TD. In this paper we describe how a combination of multilateral tool (MLT) along with CT downhole measurement system is used to locate and access laterals, selectively acquiring a gamma ray profile across each of the laterals, then optimizing the stimulation operation using distributed temperature survey (DTS) measurements. This paper discusses the planning, execution and evaluation of the results in a stimulation job performed on an open hole multilateral water injector employing a CT with fiber optics that enables a real-time downhole measurement tool (pressure, temperature, casing collar locator, gamma ray) and MLT. The results highlight the effectiveness of the technique in maximizing the wells performance.

Traditionally, first-generation inflow control devices (ICDs) were designed to balance completion pressure differential with reservoir pressure differential so that even flow across production zones could be maintained. The purpose of maintaining even flow was to delay influx of unwanted fluids, and thus, help maximize oil production. However, if low-viscosity fluids were present and did succeed in breaking through, a traditional passive ICD could not control the flow, and the unwanted fluid flow would take over. To provide more effective control in these conditions, a newly developed, autonomous Inflow- control device (AICD) has been introduced to theIndustry. This device will improve production by balancing desired production fluids across the completion, while restricting the production from zones with unwanted fluids. This paper discusses 1) how the AICD detects the fluid properties and 2) how the production of the undesired fluid can be restricted without the use of any moving parts. The autonomous ICD is designed to function without moving parts, without intervention, without electronics, and without control lines. It is a solid-state design with improved reliability, erosion resistance, corrosion resistance, and plugging resistance that can maintain high mechanical integrity. An autonomous ICD operates by detecting whether the production fluid is undesirable, and then, if unwanted, it restricts the production of the unwanted fluid. The fluidic diode-type AICD operates using a combination of fluid mechanics, computer modeling, and measured performance data, and this paper explains how these devices are capable of restricting undesirable fluid breakthrough without intervention so that a well can continue to produce the desirable hydrocarbon. Testing procedures and results, which include numerical simulation and experimental testing, also will be presented.

Technological advancement in sensors, digital electronics and wireless communications have enabled development of low cost and low power wireless sensors and networks, thereby enabling a paradigm shift in autonomous monitoring and control for a wide range of applications in the oil and gas industry. These applications can be found all across the industry including supply chain, refineries/petrochemical plants, pipelines, exploration, drilling, production and transportation. One of the challenges is powering up these sensor/control devices. Despite the ultra-low power consumption of wireless nodes and the high energy density of batteries, they still have limited stored energy and, therefore, a limited lifetime. In many scenarios, the battery replacement of sensors could be a very time consuming task and even uneconomical and unmanageable. To tackle these issues and enable fully autonomous and maintenance free wireless sensing and control, a continuous source of energy is required. We have investigated Energy Harvesting as the potential solution for providing reliable and long-term power for sensors by scavenging various ambient energy sources such as environmental/machine vibrations, thermal sources, flow, solar, wind energy and converting it to useable electrical energy. This paper provides a survey of energy harvesting techniques including mechanical (piezoelectric, electrostatic, electromagnetic and magentostrictive), thermal (thermoelectric, pyroelectric), light, and various state-of-the-art technologies. The requirements and technical challenges, along with financial drivers and benefits, are addressed. Various ambient energy sources present at the surface and their usage for different applications in the oil and gas industry are identified and discussed in detail. Finally, recommendations are made for improvement and achieving the goal of practical implementation of energy harvesting to enable self-powered remote and wireless monitoring and control in oil and gas. Although the energy harvesting market is rapidly increasing because of its growing demand in various industries, the technology hasn’t been thoroughly investigated for oil and gas applications.

In general, the operation of produced water treatment systems is often based on manual sampling, visual observations, operator judgment and manual tuning by operation of valves. Work has been conducted to automate the operation of a Compact Flotation Unit (CFU). The objective was mainly to reduce the overall amount of discharged of Oil-in-Water (OiW). As discharge peaks often govern the overall OiW percentage in discharge, a real-time operated and optimized CFU system could immediately detect and reduce the effect of such peaks. Furthermore, the waste generated and utility consumption should both be reduced. The CFU operation would not require operating personnel which is an absolute requirement for an unmanned installation or subsea operation. The work was conducted in a produced water test rig. Manual sampling and visual observations were successfully replaced with online analyzing- and flow measuring technology. This replacement ensured better process overview and allowed implementation of Integrated Operations (IO) philosophy. Advanced control algorithms for real-time CFU operation and optimization were developed based on implementation of this technology. The work has resulted in a new automatically operated CFU system. Testing in the produced water test rig met the goals: increasing OiW content was detected and effective corrective actions were taken without the need for operating personnel. In addition to reducing the OiW content, the waste generation and utility consumption were simultaneously minimized.

The dependency on well-head and field real time data has significantly increased in the past few years in the oil and gas industry. The data is used to maximize oil and gas recovery, increase revenue and improve health and safety. This new mode of operation mandates the availability of reliable real time information sent by the different well and field digital equipment for optimum right-time decision making. The challenge is in the fact that intelligent field components include many different layers and nodes that increase the complexity of such assurance. Therefore, the health and the data transmitted from these components, such as digital gauges (e.g., multi-phase flow meters and permanent downhole monitoring systems) and the communication network must be continuously monitored and well maintained. This paper will highlight a newly developed comprehensive and interactive monitoring and key performance indices reporting solution to quantify, assess and visualize the health of the different intelligent field components. The new system is using advance technologies, such as GIS maps to locate fields, equipment, real-time data historian servers, and networks to highlight their availability and reliability status. This solution provides real time alerting and alarming in case of transmission failure, well data integrity situation or inaccurate data detected using an automated diagnostic engine, thereby maximizing safety and increasing accuracy and data reliability.

Our industry has long searched for a means of evaluating the efficacy of acid stimulation of extended reach carbonate wells. Intelligent completion architectures and technologies have enabled the pre-compartmentalization of these wells, and delivered the means to selectively and remotely control zonal inflow/outflow along with the ability to make real-time measurements within these zones. This paper describes the development and in-well usage of a unique modeling technique combined with a Distributed Temperature Sensing (DTS)-enabled intelligent completion system to evaluate the coverage and effectiveness of intra-zonal acid stimulation. This system leverages a proprietary thermal model that accounts for three in-well thermal effects occurring during compartmentalized bull-head treatments. The technique quantitatively evaluates the acid distribution and acid-rock interaction in each treated zone. The acid distribution measurement can be generated immediately following the acid treatment, enabling real-time determination of the acid volume and its distribution within the target completion interval(s). The operator can then decide if retreatment(s) is needed. The paper focuses on the modeling technique for matrix acid stimulation in which acid is delivered by bull-heading down the tubing string to each pre-compartmentalized zone where sliding sleeves have been positioned at the lower end of each zone. The flow of acid into the tubing/perforated-casing annulus thus produces a counter-flow condition (within the annular space) to reach all perforations within that respective zone. A direct measurement of acid movement and placement is immediately generated based on a full-length DTS measurement within each zone during treatment. The paper also demonstrates the capability of the system in the generation of a type-curve. The operator can then qualitatively identify the under-treated or over-treated sections by comparing the DTS measurement results to the type-curve. A synthetic case study is used to demonstrate the technique. Full field applications using this model will be discussed in a future paper.