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.

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.

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).

The "M" field is located offshore East Malaysia, with a major lateral fault separating the field into North and South. The south area main reservoirs are I, K and L with strong water drive and highly unconsolidated formations. These reservoirs contain approximately 200 MMSTB STOIIP with a current recovery factor of 24%. The major challenges in this field are (1) high well deviation, (2) sand production and (3) early water break-through. In order to dynamically mitigate these challenges, a pilot well with intelligent well technology was considered for one of the new highly deviated infill wells which targeted 3 producing zones. A combination of cased-hole gravel-packing and open- hole stand-alone screens (SAS) was installed at the reservoir sections. This Intelligent Completion IC system is equipped with innovative multi-drop hydraulic modules. The first IC system, successfully installed 2 years ago in 2 wells of an offset field pilot, has had a significant impact on production to date. These systems used the conventional control line configuration of an N+1 system, where N = the number of zones to be completed. However, the newly installed IC system provides an engineering solution to reduce the required number of control lines from 4 to only 2 to control 3 zones, i.e. an N-1 control line configuration. This innovative solution was essential to the project to avoid modifications to the wellhead due to the short lead-time before the installation. This solution also reduced the installation time compared to the first edition of the IC system. This paper highlights the screening process for candidate selection and the successful installation of IC system in multi stack reservoirs with gravel pack assembly and also the lessons learned from the full collaboration between the project team and service provider.

Intelligent well completions enable production and/or injection to be controlled and optimized across multilateral reservoir zones. The Distributed Acoustic sensor (DAS) is a promising technology for a number of wellbore monitoring applications such as distributed flow profiling. The DAS system can provide real-time monitoring across several zones equipped with Inflow Control Valves (ICVs) and Inflow Control Devices (ICDs) that are used to optimize the production and extend the life of the reservoir. The new intelligent Distributed Acoustic Sensor (iDAS) uniquely allows the simultaneous recording of acoustic energy along many kilometers of optical fiber deployed along the wellbore. The system uses a novel digital optoelectronics detection technique that captures the amplitude and phase of the acoustic waves generated and propagated along the wellbore over a wide frequency (8mHz – 100kHz) range with a high dynamic range (>120dB). A number of signal processing techniques have been developed for processing a large array of acoustic data that can be recorded along the wellbore for monitoring the inflow at different zones. There are a number of intelligent well completions with fiber optic cable installations in Saudi Arabia. The DAS system described here can be retrofitted to the existing installations both with the multimode fibers, which were originally installed for the Distributed Temperature Sensor (DTS), and with single-mode fibers that are used for telemetry to point optical sensors. The paper will report on the first survey results of the DAS measurement in an intelligent well that is equipped with ICVs. The results will show how the DAS system can be used to monitor the completion components such as by listening to the inflow noise across ICVs and ICDs and indicate the inflow contributions from different zones. The paper will present the first DAS survey using multimode fibers that was installed for the DTS measurements.

Continuous well performance monitoring plays a key role in making decision related to well workover and production optimization. Well parameters and corresponding rates over a period of time will represent the change in well performance. Live Well models are useful for estimating the continuous well production rates. Well models become live if they get updated with changing fluid and reservoir properties along with proper calibration to latest well conditions. In general industry practice is to update the model manually; this is a tidious and time consuming process. Umm Gudair Field Development team implemented a real time system using available resources that integrates and runs workflows between corporate data base, well surveillance data base and well models. Workflows were implemented as part of the real time system to calculate the well parameters from sensor readings and update the models to run on daily basis, such that the models become live and production rates will be estimated. The daily output generated from the workflows is basically updated well models and parameters along with production estimation report that will get emailed to users. The daily report contains the information about well status, potential, reasons for well closure etc. The workflows are intelligent enough to flag the need for model calibration and surface rate measurements. The daily estimated well parameters will be saved back to database for visualization. In conclusion, real time system was implemented to keep the well models live and useful as a tool for optimizing the oil production, improving the ESP’s run life and delaying the well intervention requirements.

Optimizing production and understanding inflow from long horizontal wells in tight chalk reservoirs has traditionally been difficult. The horizontal sections of these wells have been typically segmented into four to eighteen zones, driven by completion, stimulation and reservoir management requirements. Surface controlled sliding side doors have been used in a number of wells to control the zones for production optimisation, but permanent down hole monitoring of each zone had not been undertaken in the Danish sector so far. The subject of this paper is the installation and application of zonal pressure gauges and distributed temperature sensing in a recently drilled long horizontal oil producer in the Danish sector of the North Sea. Despite a very challenging trajectory and reservoir pressure variations along the horizontal section the well was drilled to 24,000 ft measured depth and equipped with permanent downhole monitoring and control capabilities. The horizontal reservoir section was segmented into five zones. In each zone a surface-controlled sliding side-door and a pressure and temperature gauge were installed. A fibre-optic cable for distributed temperature sensing (DTS) along the upper four zones to a depth of around 15,500 ft was installed as well. The upper four zones also contained an extra coiled-tubing operated sliding side door for restimulation purposes. After completing the well each zone was matrix acid stimulated. Stimulation monitoring with DTS allowed quantification of zonal acid coverage and identification of the scope for re-stimulation. During initial production of the well the pressure and temperature gauges in each of the zones proved valuable for start-up operations. Cross-flow between zones was identified and this knowledge was used for improved management of commingled zonal production. This paper describes how temperature and pressure data from each zone has been used for well production optimisation, in sometimes unexpected ways. The combination of being able to react without intervention using the real-time data has proven to be critically important and supported more efficient operations.

Implementing asset-wide intelligent digital oilfield (iDOF) solutions, aiming to optimize oil and gas production system in an "intelligent" manner, requires integrating concepts from different disciplines, such as artificial intelligence. Neural network- (NN-) based models are a form of artificial intelligence, which is a branch of computer science that generates mathematical models that can be "trained" to determine relationships between inputs and outputs, recognize patterns, and perform reliable short-term predictions. NN models using real-time data are proven tools for short-term well production forecasting with acceptable accuracy. An oilfield is a hostile environment for even the most robust instrumentation. As a result, technical outages or anomalies can result in lost or poor quality data. Experience shows that many samples in a real-time database are frozen, missing, corrupted, or incorrect. This fact represents the biggest challenge to creating a reliable NN model. However, the models can be trained to correct or estimate missing real-time data. This paper presents a case study where nodal analysis was used to populate missing data used to train NN model, thus improving the reliability of the model. Because nodal analysis is not suitable for prediction, time-series analysis was used to assess the impact of historical events, and operational conditions were used to forecast trends. The NN trained with nodal analysis can cover a wide variability spectrum and, when trained with a time-lapse series, can predict short-term (30-day) production scenarios by changing highly correlated parameters, such as tubing head pressure (THP) or frequency (Freq). This paper describes training NNs using nodal analysis and time-series analysis to predict short-term water cut (WC or BS&W) and liquid flow rate. This technique was applied in over 20 wells with electronic submersible pumps (ESPs) and gas lifts (GLs). The NNs made robust estimates of production rate and an acceptable prediction trend for 30 days, even when confronted with flow meter instrumentation failure, lost signals, and out-of-calibration instruments. Hence, the NN served as a "virtual meter," providing instantaneous and accurate estimation of production data.

Distributed Acoustic Sensing (DAS) technology from optical fibers is already identified as very promising for reservoir monitoring 1 and especially for seismic applications 2 with many stakes today in all Oil&Gas companies. DAS surveys can also have more "production" oriented applications for in-well surveillance and more specifically in complex well configurations and completions. This paper relates an in-well trial from permanent optical fibers installed in a horizontal, multi-lateral and ESP (Electrical Submersible Pump) activated well. Two types of fibers had been installed permanently in this well: a multi mode fiber and two single mode fibers linking FBG’s (Fiber Bragg Gratings) optical gauges. These fibers were used to simultaneously acquire distributed temperature from DTS and distributed acoustic data from DAS. The first objective of this in-well trial was to use the DAS data to improve or better constraint the temperature interpretation to get more reliable flow profiles. The acquisition sequence was designed to generate several temperature disturbances using different ESP frequencies leading to different production rates. DTS interpretation has been performed and results are now more in accordance with previous interpretations. However, the ESP location introduced noise which made difficult to interpret the DAS signal from the close producing zones. For that reason, the first objective was not fully reached. It should be noticed that this was the first DAS acquisition in a well equipped with an ESP. The paper describes also how DAS data were investigated to open a new application on the understanding of pump behavior and possible optimization of pumps.

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.