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Results
Abstract Converting data to actionable information through continuous oil production monitoring is a fundamental part of any production optimization strategy. The development of Intelligent Field technology has remarkably contributed to the upgrading of production surveillance framework and provided an extended access to real-time data. This same technology is still in its infancy when it comes to multiphase mass metering and field practicality issues. As for conventional fields where the unavailability of continuous data flow is not considered out of norm, the high uncertainty in oil production rate estimation and allocation is very well expected. The main source of this uncertainty is the reliance on sporadic welltest data and empirical multiphase flow correlations to allocate liquid production rate. Critical and subcritical multiphase flow choke performance is predicted using well-known correlations that are based on specific datasets characterized by a specific field or hydrocarbon type. Case studies where those correlations are matched with different production data and used later to predict the choke performance are present in the literature. Yet, the oil industry is faced with many challenges because of the limited accuracy of those predictions. The complexity of multiphase flow behavior and the irregularities in operational conditions can explain such low capability of those correlations particularly on field data. Artificial intelligence (AI) tools and techniques for so-called artificial neural networks, fuzzy logic and functional networks were employed to develop data-driven oil flow rate computational models for both critical and subcritical flow conditions. These AI models were trained and tested exploiting 595 production rate tests from 31 different wells. The prediction results showed a strong correlation with actual field data and promised a reliable tool/methodology to estimate oil flow rate as a function of operational conditions and choke size. This paper presents an engineering look at the inclusion of AI data-driven models in the production surveillance system to enhance welltest data validation and reduce the uncertainties in production allocation.
- Production and Well Operations > Well & Reservoir Surveillance and Monitoring > Production logging (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (1.00)
Abstract A new generation of a near real-time, cost-effective distributed downhole temperature and pressure measurement system that utilizes improved microchip technology was developed in the laboratory and tested in the field. The improved microchip technology splits temperature and pressure measurement into two individual systems. Design of the printed circuit board and the electrical components has minimized the measurement error and power comsumption of the system. Significant improvements in the integrity of the drilling microchip have been achieved by using a new protective material. Experimental results show that the accuracy of the system measurement is within ยฑ0.5ยฐC for temperature measurements and ยฑ0.05% for pressure measurements. The drilling microchip is typically deployed by using a tracer injection system continuously (controlled by a computer) or by dropping it into the drillpipe while making a pipe connection during rig operation. It travels along the inner passage of the drillpipe, exits the bit nozzle, and returns toward the surface in the wellbore annulus because of dynamic circulation with drilling fluid. Then, it is usually captured at the shale shaker at the surface. A total of 14 new generation tracers were deployed into two different wells in two batches (4 pieces and 10 pieces) during the field evaluations in Saudi Arabia. Nine of the deployed 14 microchips were recovered at the surface with the measurement data stored in the on-chip memory. The results of the field tests proved that the microchip system is able to survive in a well with 13,800ft vertical depth under more than 150ยฐC bottomhole temperature and 10,000 psi bottomhole pressure conditions. Measurements retrieved from the recovered microchips show excellent consistency. The circulating mud temperature in the field test well has also been predicted by using existing thermal model. The modeling result has been compared to the microchip measurements and it shows a strong agreement between two temperature profiles inside the drillpipe. However, the modeling result is probably not accurate in the annular section based on comparison to the microchip measurements. The new generation microchip system developed is ready to be deployed in most oilfield conditions to provide a downhole in-situ circulating mud temperature and pressure (i.e., equivalent circulating density) profiles for optimizing cement formulation as well as identifying hole problems such as seepage, lost circulation zones, and open-hole restriction due to poor hole cleaning or an under-gauged hole.
- Asia > Middle East > Saudi Arabia (0.48)
- North America > United States > Texas (0.46)
Objectives/Scope: 1. Outline challenges O&G Big data brings and emerging requirements 2. Propose alternative approach for Big Data processing at the remote sites and why Cloud is not enough 3. Demonstrate O&G use cases that benefit from the proposition Methods, Procedures, Process: Fog computing is a new and growing technology area designed to address requirements for processing at the edge of growing amounts of field data (O&G Internet of Things), and to address new applications that have low-latency requirements and can not be addressed by cloud infrastructure typical for other industries. Fog is not a replacement of the cloud, but an extension. It allows O&G companies to extend analytics, expand context awareness, improve data quality at the remote site (Oilfield, Offshore Drilling/Production Platform) Results, Observations, Conclusions: Key areas and emerging O&G application that benefit from Fog phenomena, and currently piloted by O&G majors and automation & instrumentation suppliers: โข Real-time actionable analytics โข Real-time data sharing between different applications, vendors and service providers โข Wellhead optimisation โข Fleet mobility and management โข Leak detection and distributed safety systems โข Employee safety โข De-manning and automation โข Secure, federated data ownership The technology fundamentally fits so well into the O&G industry as its operations are taking place in remote geographies, and there is a clear need for effective combined local and remote data processing, especially triggered by growing volumes of data at the edge. As an example, Distributed Acoustic Sensor for Vertical Flow Profiling use case is capable to generate around 1-2 TB of data per day per well. However, typical satellite channel to the data centre is around 250K-1Mb. Fog Computing enables pre-processing of the data and distributed analytics at the edge, thus optimising data transmission, however allowing geo scientist still to inquire high resolution data from remote site, in case there is an interest to zoom into particular area. Novel/Additive Information: Fog Computing is a new and revolutionising phenomena for field remote processing of data at scale. It is going through pilot stage, and O&G companies are loading it with multitude of use case and applications to explore full potential and develop competitive advantage with more agile platform for data insights and knowledge.
- Production and Well Operations > Well & Reservoir Surveillance and Monitoring > Production logging (0.90)
- Data Science & Engineering Analytics > Information Management and Systems > Data mining (0.83)
- Information Technology > Information Management (1.00)
- Information Technology > Architecture > Real Time Systems (0.95)
- Information Technology > Data Science > Data Mining > Big Data (0.83)
- Information Technology > Communications > Networks > Sensor Networks (0.50)
Abstract An operator had a well producing 100 percent water-cut and wanted to identify the origin of the water production using fiberoptic sensing technology. The vertical well was cased, perforated, and hydraulically fractured. A production string that included an electric submersible pump (ESP) at the bottom was run into the hole to a depth immediately above the top perforations. An additional 1,500 ft. of one-inch diameter rods were hung from the bottom of the ESP to convey fiber-optic cable into the perforated region. Fiber-optic cable was clamped onto the outside of the production tubing, ESP, and rods as they were being run in hole. Distributed temperature sensing (DTS) and distributed acoustic sensing (DAS) data were collected from the fiber and used to identify the water ingress locations using several techniques including DAS energy computation, DTS temperature trace evaluation, and DTS heat transfer models. The analytical methods were in agreement on the distribution of fluid entering the well. The effect of an operating ESP on DAS measurements was also investigated.
- Geophysics > Borehole Geophysics (0.93)
- Geophysics > Seismic Surveying > Passive Seismic Surveying (0.71)
- Well Completion (1.00)
- Production and Well Operations > Well & Reservoir Surveillance and Monitoring > Production logging (1.00)
Cloud-Based Solution for Permanent Fiber-Optic DAS Flow Monitoring
Richards, J.. (OptaSense) | Bartlett, R.. (OptaSense) | Onen, D.. (OptaSense) | Crowther, G.. (OptaSense) | Molenaar, M.M.. M. (Shell Canada) | Reynolds, A.. (Shell Exploration and Production Co) | Wyker, B.. (Shell International E&P) | den Boer, H.. (Shell International E&P) | Berlang, W.. (Shell International E&P)
Abstract In 2014, a first permanent installation of a fibre-optic (FO) distributed acoustic sensing (DAS) system was piloted in a tight gas well in Northern British Columbia. The project had three goals; to permanently monitor flow rates along the entire well bore, to make that information available to collaborative teams worldwide in real-time and to advance the system for future installations. In oil and gas field development there is often a lack of frequent quality well and reservoir surveillance (WRS) data for quality decision making; leaving significant reservoir or well performance uncertainties potentially leading to suboptimal reservoir development. The need for frequent and good quality surveillance data is highest in complex reservoir developments such as unconventional plays, water-flooded reservoirs, thermal and chemical Enhanced Oil Recovery projects. Often, well surveillance data is not acquired in practice because of concerns associated with production deferment, costs & logistics, HSE exposure or because it creates operational risks associated with well interventions when using conventional logging methods. The attractiveness of FO-based surveillance lies with the fact that once the passive FO cable has been installed, no subsequent well interventions are required to collect downhole data; allowing for continuous (long-term) measurements or repeated measurements as and when required while eliminating the concerns associated with conventional logging methods. The pilot system deployed at the well site is continuously measuring and recording qualitative and quantitative flow information. Using a secure web browser, the asset team can access the real-time and historical data when required or share with collaborative teams worldwide. The pilot has helped identify where improvements can be made in the enabling Distributed Sensing infrastructure such as handling and evaluation of the large data volumes, seamless data transfer, the robustness of the system installation and the overall integration of data into the full workflows. It will take further development of the system to implement all these improvements, but it is clear that FO based applications will play a key role in future well and reservoir surveillance. This paper presents the system architecture and details the lessons learnt in designing, commissioning and running this system including the extraction of low data rate, actionable, qualitative data from distributed fibre-optic sensors and the IT challenges of creating a reliable, permanently installed system.
- North America > United States (0.28)
- North America > Canada > British Columbia (0.25)
Abstract Knowing the exact flow allocation for each controlled zone is important for well optimization and the management of an intelligent well system (IWS). For two-zone IWS producers, a broadly accepted downhole gauge configuration uses the triple-gauge system, where two gauges give the upstream side pressure/temperature (P/T) of the two downhole control valves, and one gauge gives the P/T inside tubing of the commingled fluid. Theoretically, this configuration gives the P/T boundary conditions between the two valves and the gauge carrier, where flow allocations can be solved numerically, based on the gauge readings and control valve settings. However, from what we have seen in the past 10 years of IWS applications, only a few have successfully published application cases regarding this topic. Is this an indication that a large number of two-zone triple-gauge IWS wells are operating in the low-confidence region of the two zone's production flow allocations? In this work, a comprehensive hydraulic model has been developed to address this topic. This paper will discuss a recent application of such a model to estimate the flow allocations of an existing two-zone deep-water IWS oil producer. The well began production in 2007. A total of 1,362 daily triple gauge data points are available for this study, where the monitored P/T data indicates that the well was flowed in multiphase conditions at downhole for a large percent of its production life. Verification was completed by comparing the predicted flow allocation results with this well's measured total rates and daily allocation rates. Further comparisons of the zonal allocations, between the model calculated results versus the zonal reservoir deliverability study predicted results, were also provided. These comparisons showed an excellent match between the predicted results, measured data, and the available reservoir study results. Descriptions of key factors to address the accuracy of the method have been provided, including compensated differential pressure, multiphase choke model, choke discharged coefficient, and fluid pressure-volume-temperature (PVT) behavior impact. A modified multiphase choke model was proposed in this study. The authors believe it will be more suitable for downhole valve operating and multiphase flow conditions. This case study has proven a very promising independent solution for continuous well rates estimation, with the solution based purely on choke pressure drops and intelligent well valve positions. The downhole monitoring P/T is normally based on seconds, which means that intelligent well flow allocations can be calculated in real-time without installing downhole venturi flow meters that may jeopardize well profitability and integrity. This solution brings measurable benefits for those IWS wells with no downhole flow meters, when taking into account the time and effort spent on periodic production tests, reservoir/well deliverability studies for production allocations, and potential production loss during the production tests.
- Asia (0.93)
- North America > United States (0.68)
- Well Completion > Completion Monitoring Systems/Intelligent Wells > Real-time optimization (1.00)
- Well Completion > Completion Monitoring Systems/Intelligent Wells > Downhole sensors & control equipment (1.00)
- Reservoir Description and Dynamics > Fluid Characterization > Phase behavior and PVT measurements (1.00)
- (2 more...)
Virtual Metering System Application in the Ceiba Field, Offshore Equatorial Guinea
Petukov, Anton (Hess Corporation) | Saputelli, Luigi (Hess Corporation) | Hermann, Jeffrey (Hess Corporation) | Traxler, Alex (Hess Corporation) | Boles, Kim (Hess Corporation) | Nnaji, Obiageli (Hess Corporation) | Vrielynck, Bruno (Hess Corporation) | Venugopal, Deepak (Belsim)
Abstract Well rate surveillance is essential for reservoir characterization and selecting potential activities to enhance and optimize production. However, such variables usually lack consistent or direct field measurements, which is related to technology availability, equipment reliability, and cost control. As a result, many technologies have been developed to estimate well rates from indirect measurements (e.g., virtual metering or soft sensors). The well rate estimation requires consistent pressure-volume-temperature (PVT) data, fit-for-purpose production well tests, and reliable sensors. In most cases, field data are used to โtuneโ data-driven models. Missing, biased, or failing sensors may break the rate estimation, and a new calibration would be required. In addition, sensor input uncertainty and rate estimation confidence were commonly overlooked in previous approaches. This paper discusses the implementation of a data validation and reconciliation pilot study in the Ceiba Field to estimate well rates. In this case, data, uncertainty, and models are combined to minimize a global error function. Rigorous statistics are used to calculate new sensor estimates. Unlike previous well rate estimation approaches, field-collected data are validated and corrected using physical models. The pilot technical scope included calculating oil, water, and gas rates for each well; calculating the tolerance of rate measurements and gauge readings; and identifying sources of unreliable measurements. Although the approach is not new in the petrochemical industry, the application is โyoungโ in the upstream. Project benefits included less downtime due to well testing and early problem detection, 65% less time expended on validating well tests and allocating individual well rates, and improved cost control due to calculating well-produced volumes hourly. These findings provide a better understanding of reservoir and well performance, which facilitates production optimization management. This paper presents a summary of current project status, the lessons learned during pilot implementation, and the procedure for further progression. Project success criteria, application key performance indicators (KPIs), and expected benefits are reviewed and analyzed. As far as they can be evaluated at this stage, they were all achieved successfully.
- North America > United States (1.00)
- Africa > Equatorial Guinea > Gulf of Guinea (1.00)
- Overview (0.54)
- Research Report (0.34)
- Energy > Oil & Gas > Upstream (1.00)
- Materials > Chemicals > Commodity Chemicals > Petrochemicals (0.74)
- Africa > Equatorial Guinea > Gulf of Guinea > Rio Muni Basin > Okume Complex > Oveng Field > Block G > Oveng Field (0.99)
- Africa > Equatorial Guinea > Gulf of Guinea > Rio Muni Basin > Okume Complex > Oveng Field > Block G > Okume Field (0.99)
- Africa > Equatorial Guinea > Gulf of Guinea > Rio Muni Basin > Okume Complex > Oveng Field > Block G > Okume Complex (0.99)
- (72 more...)
Abstract The diatomite reservoir in the Belridge field, California, has been undergoing water injection for pressure maintenance to mitigate reservoir compaction and improve oil recovery. The reservoir is over one thousand feet thick with multiple layers, high compressibility, and very low permeability. Accurate placement of injection water across this massive reservoir is essential for balancing layer by layer voidage and reducing compaction. Therefore, monitoring sub-surface injection profile has become an important part of diatomite waterflood surveillance. However, monitoring profile with conventional wireline radioactive tracer tools has proven to be challenging due to the inability to access wellbores for logging because of scale build-ups or casing deformations. Over the past several years, a number of field trials have been performed to see if injection profile could be monitored using distributed temperature sensing (DTS) fiber-optic technology. If the technology works, then the strategy would be to install the DTS fiber early in the life of a well while the whole wellbore was still accessible. Once the fiber was in place, dynamic monitoring of injection profile could continue even if the well later developed scale build-ups, dog-legs, or other obstructions. Initial tests at Belridge were done with the DTS cable temporarily deployed on slickline. Once it was established that DTS could be used to measure injection profile in diatomite, several permanent installations were made in different areas of the field and in injectors with different mechanical configurations. Also, three different analysis methods were tried: stabilized injection; thermal restoration; and thermal tracer. In all cases, DTS-derived injection profiles were compared against wireline radioactive tracer profiles run at about the same time and under similar injection rates and pressures. Based on the technical success of the pilot, it was decided to scale-up to a 25-well program prior to full-field implementation in all 1000+ injectors in Belridge. This scaled-up program was focused on retrofitting DTS in existing injectors that still have an unobstructed well bore. These installations required the DTS cable to be run inside the injection tubing to the current effective depth of the well. However, the presence of the fiber-optic cable inside the tubing made the well unserviceable for future interventions such as coil-tubing clean-out, stimulation, or cased-hole logging operations. For this reason, design work is currently under way to run the DTS cable outside the injector casing at the time of initial drilling and completion of the well. This paper is a case study of the application of a new technology in solving surveillance issues in an old field. It covers the slow but methodical implementation of the DTS project, the challenges, and our solutions. It presents many examples of injection profiles derived from DTS measurements and a comparative evaluation of different interpretation techniques. The learnings from this project have potential for application in other secondary or tertiary recovery operations that require measurement of injection profile or continuous monitoring of the injection front.
- North America > United States > Texas (1.00)
- North America > United States > California > Kern County (0.61)
- Energy > Oil & Gas > Upstream (1.00)
- Water & Waste Management > Water Management > Lifecycle > Disposal/Injection (0.35)
- North America > United States > California > San Joaquin Basin > Belridge Field (0.99)
- North America > United States > California > Salinas Basin > Coalinga Field (0.99)
- Europe > United Kingdom > England > Hampshire Basin > PL 089 > Block 98/6 > Wytch Farm Field > Sherwood Formation (0.99)
- (11 more...)
- Well Completion > Completion Monitoring Systems/Intelligent Wells > Downhole sensors & control equipment (1.00)
- Reservoir Description and Dynamics > Improved and Enhanced Recovery > Waterflooding (1.00)
- Production and Well Operations > Well & Reservoir Surveillance and Monitoring > Production logging (1.00)
Abstract Simulating automated action of smart well components represents a challenge in forecasting performance of such wells, which is fundamental in their design decisions. For example, wells equipped with Inflow Control Valves (ICV) where zones have to be switched on, off or partially closed based on their performance relative to the rest of the wells/completions in the field which share the same surface network and facilities constraints. In this paper we present a study that has been carried out to justify installation of a surface controlled ICV in a group of wells in an off-shore Australian field with commingled production. The merit of surface controlled ICV versus uncontrolled commingled production has been compared. A numerical reservoir simulator has been used to model reservoir performance and production from individual zones. Also, well and production network has been simulated using a well and Production Network Flow Simulators. An interface "simulation manager" is used to facilitate information exchange between the two simulation programs and optimization of the process. Proper control of ICVs is simulated based on reservoir and well-bore simulation data which will result in maximum oil production of field network system resulting in higher recovery. Also, we have done typical economic analysis for smart well completion implementation. The results show that smart completion is viable for this field. Introduction A smart/intelligent well is a non- conventional well with down-hole devices as sensors, ICVs to give ability of continuous in-situ monitoring of fluid flow rates, pressures and periodic adjustment of down-hole valves. Smart wells have shown several benefits in practical applications, especially for multiple reservoirs where the main production strategy is commingled production. The control valves are in "on", "off" or "partially closed" modes based on the need as dictated by the pre-defined objective functions.. The applicability of smart completions is not confined to commingled production (Yeten et al, 2002). Their potential benefits have been elaborated in several publications. Since this technology has a lot of issues regarding its reliability and cost effectiveness,, many companies tend to apply this technology with step-wise justification, knowing that the cost of a smart completion is three or four times more expensive than conventional one. To make a decision to instrument a well with smart equipments several aspects, including purpose and justification, must be investigated (Anderson, 2007). In this paper we present a study that has been carried out to justify installation of a surface controlled ICV in a group of wells in an offshore Australian field with co-mingled production. The merit of surface controlled ICV versus uncontrolled commingled production has been compared. A numerical reservoir simulator has been used to model reservoir performance and production from individual zones. Also well and production network has been simulated using a well and Production Network Flow Simulator. An interface "simulation manager" used to facilitate and control information exchange between the two simulators. The final result of each time is output through the "simulation manager". Proper control of ICVs is simulated based on reservoir and wellbore simulated data which will result in maximum oil production.
- Oceania > Australia > Western Australia (0.28)
- North America > United States > Texas (0.28)