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Abstract Production from Artificially lifted (ESP) well depends on the performance of ESP and reservoir inflow. Realtime monitoring of ESP performance and reservoir productivity is essential for production optimization and this in turn will help in improving the ESP run life. Realtime Workflow was developed to track the ESP performance and well productivity using Realtime ESP sensor data. This workflow was automated by using real time data server and results were made available through Desk top application. Realtime ESP performance information was used in regular well reviews to identify the problems with ESP performance, to investigate the opportunity for increasing the production. Further ESP real time data combined with well model analysis was used in addressing well problems. This paper describes about the workflow design, automation and real field case implementation of optimization decisions. Ultimately, this workflow helped in extending the ESP run life and created a well performance monitoring system that eliminated the manual maintenance of the data. In Future, this workflow will be part of full field Digital oil field implementation.
- North America > United States > Texas > Permian Basin > Yeso Formation (0.99)
- North America > United States > Texas > Permian Basin > Yates Formation (0.99)
- North America > United States > Texas > Permian Basin > Wolfcamp Formation (0.99)
- (21 more...)
Abstract Methodologies and numerical tools are available (1) to construct geologically realistic models of fracture networks and (2) to turn these models into simplified conceptual models usable for fieldscale simulations of multiphase production methods. A critical step remains however, that of characterizing the flow properties of the geological fracture network. The multiscale nature of fracture networks and the associated modeling cost impose a scale-dependent characterization: (1) multiscale fractures that may be characterized in local dynamic test areas, e.g., drainage areas involved in well tests, through the calibration of geologically realistic fracture models; and (2) large-scale faults that are characterized through reservoir-scale production history simulations that involve upscaled flow models with an explicit fault representation. However, field data are commonly insufficient to fully characterize the multiscale fracture properties. Therefore, efficient inversion methodologies are necessary to sample wide ranges of property values and to characterize a variety of solutions, i.e., fracture models that are consistent with dynamic data. This article presents an inversion methodology to facilitate the characterization of fracture properties from well-test data. A genetic optimization algorithm has been developed and coupled with a three-dimensional fracture model upscaling simulator to perform the simultaneous calibration of well-test data, i.e. equivalent transmissivities K· h, with K the equivalent permeability that takes into account fracture flow properties, and h the reservoir thickness over which the well test has been interpreted. Several genetic crossover and mutation strategies were studied and tested on three geologically realistic fractured reservoir models, involving both small-scale diffuse fractures and large-scale sub-seismic faults. The characterized diffuse fracture properties are mean length, mean conductivity, orientation dispersion factors, and facies-dependent properties such as fracture density. The fault network conductivity is also characterized. The effectiveness of this inversion methodology to characterize physically meaningful and data-consistent fracture properties is discussed.
- Geology > Rock Type (0.46)
- Geology > Structural Geology > Fault (0.34)
The testing flare burns brightly during a methane hydrate production test of the Ignik Sikumi No. 1 well on the Alaskan North Slope. A production method that could unlock large reserves of methane hydrate in sand-dominated reservoirs was tested successfully from a scientific and operational standpoint in a recent research experiment on the Alaskan North Slope (ANS). The experiment was conducted by the National Energy Technology Laboratory (NETL) of the United States Department of Energy (DOE) in partnership with ConocoPhillips and Japan Oil, Gas, and Metals National Corporation. A proof-of-concept test was conducted between 15 February and 10 April at the Ignik Sikumi No. 1 well in the Prudhoe Bay field operated by ConocoPhillips. The production technique featured the injection of carbon dioxide (CO2) to exchange and release methane (CH4) from the hydrate, a method developed through laboratory collaboration between the University of Bergen in Norway and ConocoPhillips. The released gas was then produced by means of reservoir depressurization. “The test objective was to perform injection and flow-back from a single well to validate that the CO2/CH4 exchange mechanism demonstrated in laboratory tests will occur in a reservoir of natural methane hydrates,” said Ray Boswell, technology manager for gas hydrates at the NETL. It was the first field-level trial of a production method involving the exchange of CO2 with the methane molecules contained in a methane hydrate structure. “The focus of the test, including the design of the well, was on the technical feasibility of this new technology, rather than an attempt to produce gas at commercial rates,” Boswell said. CO2 Mixture Injected in Reservoir The Ignik Sikumi well test was equipped with downhole fiber-optic distributed temperature and acoustic sensing, three downhole pressure gauges, and full surface instrumentation, including high-resolution in-line gas chromatography. Over a 13-day period, a carbon dioxide/nitrogen mixture was successfully injected into the 30-ft-thick reservoir interval, saturated with methane hydrate, without loss of injectivity. This was followed by a production stage in which the pressure was held above the stability pressure of the in-situ methane hydrate. CH4 was produced during this stage, and initial data analyses indicated that CO2 exchange was achieved. Ongoing analyses of the extensive datasets acquired at the field site are under way to determine the overall efficiency of simultaneous CO2 storage/CH4 production from the reservoir. As part of the demonstration, the depressurization phase of the test extended for 30 days. The longest previous field test of depressurization to extract gas from hydrate lasted 6 days as part of a Japanese-Canadian testing program at the Mallik well in Canada’s Northwest Territories during 2007 to 2008.
- Government > Regional Government > North America Government > United States Government (1.00)
- Energy > Oil & Gas > Upstream (1.00)
- North America > United States > Alaska > North Slope Basin > Milne Point Field > Kuparuk Formation (0.99)
- North America > Canada > Quebec > Arctic Platform (0.89)
- North America > Canada > Nunavut > Arctic Platform (0.89)
Advances in Production Monitoring: A Deepwater Field Case History
Udofia, Emmanuel E. (Shell Nigeria E & P Co) | Van Den Berg, Frans G. (Shell Intl. E&P Co.) | Oguntimehin, Adegbenro B. (Shell Petroleum Company Nigeria) | Beijer, Vincent (Shell Nigeria E & P) | Cor, Koster (Shell Nigeria) | Oni, Olatunbosun (Shell Nigeria E & P)
Abstract Bonga is a deepwater field, which lies 120km southwest of Warri in Nigeria at awater depth of 1000m. Bonga started production in November 2005 with sustainedwater injection for pressure maintenance and was the first deepwater project inNigeria. Production Universe which is a state-of-the-art Shell productionmonitoring tool was deployed as part of the Bonga Smart Field's FoundationMark-1 implementation and this has been used for real-time productionmonitoring and optimization. The Smart Fields Foundation project provides theBonga asset with accurate data and models for production surveillance andoptimization. Real-time production surveillance and optimization has always been a challengeto efficient well and reservoir management in the oil and gas in industry. Conventional method of production monitoring in the oil and gas industry is toline a well through the test separator and acquire the required data. We thenerroneously assume that production from this well(s) remains constant(regardless of the prevailing situation affecting this well) during the periodof interest and we use this rate for monitoring. However, despite the complexnature of well testing in Bonga (with the support of a real-time ‘virtualmeter’ called Production Universe), we have been able to demonstratesignificant improvement in production monitoring and optimisation as shown inthe improved production reconciliation factor between estimated rates fromProduction Universe compared to the actual field production reconciled volumes. Bonga is a field with varying flow rates in the producer wells with very fastresponse to changes in water injection rates. Hence, accurate production monitoring significantly improved the Bonga fieldWell and Reservoir Management process. The result could be applied to similarassets with the benefit of sustained real-time production monitoring.
- Asia (1.00)
- Africa > Nigeria (1.00)
- North America > United States > Texas (0.69)
- North America > United States > Arkansas (0.47)
- Energy > Oil & Gas > Upstream (1.00)
- Water & Waste Management > Water Management > Lifecycle > Disposal/Injection (0.57)
- Africa > Nigeria > Gulf of Guinea > Niger Delta > Niger Delta Basin > OML 118 > Bonga Field (0.99)
- North America > United States > Arkansas > Smart Field (0.98)
Advances in Production Allocation: Bonga Field Experience
Udofia, Emmanuel (Shell Nigeria Exploration & Production Company) | Akporuno, Mamoke (Shell Nigeria Exploration & Production Company) | Vandenberg, Frans (Shell Nigeria Exploration & Production Company) | Beijer, Vincent (Shell Nigeria Exploration & Production Company) | Oguntimehen, Gbenro (Shell Nigeria Exploration & Production Company) | Oni, Olatunbosun (Shell Nigeria Exploration & Production Company)
Abstract Bonga field located in deepwater of the Niger Delta in Nigeria, started production in November 2005 and from inception with sustained water injection for pressure maintenance. By 2006, Production Universe which is a state-of-the-art Shell production monitoring and allocation tool was deployed as part of the Bonga Smart Field's Foundation Mark-1 implementation and this has been used for Bonga production allocation process. The Smart Fields Foundation Mark-1 project aimed to provide the Bonga asset with solid data and models for production surveillance and optimisation. Accurate production allocation has always posed a threat to efficient well and reservoir management in the oil and gas in industry. The traditional approach to production back-allocation to individual wells and reservoir is usually based on ‘one-point’ well test data with the clear assumption that production from this well(s) remains constant (regardless of the prevailing situation affecting this well) for the period of interest. Meanwhile, in Bonga with the support of a real-time ‘virtual meter’ called Production Universe, we have been able to demonstrate significant improvement in our production allocation process as shown in the improved production reconciliation factor between estimated and measured field production data. Bonga is a field with varying flow rates in the producer wells with very fast response to changes in injection rates. Consequently, accurate production allocation significantly improved the Bonga well and Reservoir Management process. The result of this paper is empirically based supported by data and shared experiences in Bonga field and could be applied in other similar operating assets with the benefit of improved production allocation.
- Energy > Oil & Gas > Upstream (1.00)
- Water & Waste Management > Water Management > Lifecycle > Disposal/Injection (0.36)
- Africa > Nigeria > Gulf of Guinea > Niger Delta > Niger Delta Basin > OML 118 > Bonga Field (0.99)
- North America > United States > Arkansas > Smart Field (0.98)
Abstract Steam injection is the primary technique used to produce heavy oil reservoirs. The technology, used either as a continuous injection or cyclic steam, has been successfully applied in Chevron’s San Joaquin Valley fields since the mid 1960’s. In the Diatomite reservoirs cyclic steam was introduced in the mid 1990’s and used profitably ever since to increase recovery from these assets. In the early times the field practices manually solved the production and steam optimization problem on a daily basis while taking into consideration all the operational constraints. As the number of wells became larger (>500) manually resolving issues became difficult. In 2003 a scheduling tool based on Genetic Algorithm (GA) was successfully deployed and is still being used. As the field has matured, the well’s individual production curve behavior has been changing from an idealistic exponential decline to a more complex profile. These new conditions together with other tool limitations prompted the opportunity to develop a new approach that would increase the success of the scheduling tool. A new approach, analogous to the Type Curves Concept (TCC) for cycle production response is introduced in this article. The methodology uses Neural Networks (NN) to identify different cycle production shape patterns. Data from 500+ wells and more than 12,000 cycles were used to identify the type curves or shape patterns. A meticulous classification of raw data revealed fourteen patterns, which were reduced to four representative patterns for further analysis and modeling. Significant data processing such as ID cycles, interpolation, normalization for amplitude and cycle-time, as well as selection of input parameters, were performed in order to train the Neural Network. To complete the model, heuristics rules were inferred to optimize well candidate selection. The hybrid NN-rule based application was coded and integrated with the scheduler and is currently being field-tested. Preliminary results indicate an 80% success rate while the business value analysis showed significant optimization potential. The paper outlines the methodology used in the study including patterns identification, model training, rule-based system and concludes with early deployment results. The hybrid system proved to provide better steam allocation and minimize production loss. Lastly, challenges and lessons learned during the development and deployment are summarized.
- Reservoir Description and Dynamics > Improved and Enhanced Recovery > Thermal methods (1.00)
- Production and Well Operations > Well & Reservoir Surveillance and Monitoring (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Neural networks (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (1.00)