Layer | Fill | Outline |
---|
Map layers
Theme | Visible | Selectable | Appearance | Zoom Range (now: 0) |
---|
Fill | Stroke |
---|---|
Collaborating Authors
Search Production data management: Pages with reference errors
...s of both models are shown in Figures 16 and 17, respectively. The true results computed from the reference fine-scale model are plotted as the thick, light curves. It is evident that the reservoir models n...loser to the true results with significantly less uncertainty. The low permeability barrier in the reference find grid model between the injection well and W1 is not well captured in the inverse coarse grid... models when the injected water is at pore volume injected (PV J) of 1.0. The true values from the reference field are shown in the same figure by bullets. The accuracy and uncertainty of forecasting are la...
... at well i 1,..., ny and time t ty, ..., th. [Wl]: is the inverse covariance matrix of observation errors at time f. If pressure measurement ...errors at different wells are independent, {W)]. is a diagonal matrix with the form of Wit [Wh ot Wn,,...ient of the objective function on the null space of the gradients of the binding constraints (see reference 9 for details). (B-5)...
...aints and the static data as well. A review of available inverse techniques has been presented in reference '8. In this paper, the Sequential Self-Calibration (SSC) inverse technique is adapted to invert pe...
Abstract This paper presents a methodology to generate maps of high resolution permeability from multiple well single-phase flow rate and pressure data. The dynamic, i.e. temporal, production data contains important information about the interwell permeability distribution that should be integrated with static data, such as well and seismic data, to generate reservoir models to provide reliable input to reservoir simulation and reservoir management. A two-step procedure is proposed for such data integration:establish the spatial constraints on large-scale permeability trends due to the production data using an inverse technique, and construct the detailed geostatistical reservoir models subject to those spatial constraints using geostatistical techniques. The single-phase pressure and production data could be provided by permanent pressure gauges, simultaneous multiple well tests, or flow rates under primary depletion. Production data and reservoir petrophysical properties, specifically permeability. are nonlinearly related through flow equations. Establishing the spatial constraints on permeability due to production data calls for the solution of a difficult inverse problem. This paper adapts the Sequential Self-Calibration (SSC) inverse technique to single-phase multiple- well transient pressure and rate data. The SSC method is an iterative geostatistically-based inverse method coupled with an optimization procedure that generates a series of coarse grid 2-D permeability realizations, whose numerical flow simulations correctly reproduce the production data. Inverse results using two synthetic data sets show this SSC implementation to be flexible, computationally efficient, and robust. Fine-scale models generated by down-scaling the SSC generated coarse-scale models (using simulated annealing) are shown to preserve the match to the production data at the coarse-scale. Finally, reservoir performance prediction results show how the integration of production data can dramatically improve the accuracy of production forecasting with significantly less uncertainty. Introduction Optimal reservoir management requires reliable performance forecasts with as little uncertainty as possible. Incomplete data and inability to model the physics of fluid flow at a suitably small scale lead to uncertainty. Uncertainties in the detailed description of reservoir lithofacies porosity, and permeability are large contributors to uncertainty in reservoir performance forecasting. Reducing this uncertainty can only be achieved by integrating additional data in reservoir modeling. A large variety of geostatistical techniques have been developed that construct reservoir models conditioned to diverse types of static data including hard well data and soft seismic data. Commonly, a number of techniques are applied sequentially to model the large reservoir geometry, the lithofacies, and then petrophysical properties such as porosity and permeability. However, conventional geostatistical techniques including Gaussian, indicator, annealing-based, or object-based methods are not suited to directly integrate dynamic production data. Production data and reservoir petrophysical properties are related to each other through flow equations which are highly nonlinear. As a consequence, accounting for dynamic engineering data in geostatistical reservoir modeling is a difficult inverse problem. Nevertheless, historical production data are often the most important information because they provide a direct measure of the actual reservoir response to the recovery process that form the basis for reservoir management decisions. Integrating dynamic production data is an important outstanding problem in reservoir characterization. Ideally, we want to directly match all types of production data in the reservoir model at the required resolution simultaneously with other types of geological and geophysical data. A number of inverse techniques have been developed for this purpose. P. 115^
- Europe (1.00)
- North America > United States > Texas (0.46)
- Reservoir Description and Dynamics > Reservoir Simulation (1.00)
- Reservoir Description and Dynamics > Reservoir Fluid Dynamics > Flow in porous media (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Geologic modeling (1.00)
- Production and Well Operations > Well & Reservoir Surveillance and Monitoring > Production data management (1.00)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Spatial Reasoning (0.75)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Constraint-Based Reasoning (0.75)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning > Gradient Descent (0.34)
...cters well name, lease, operator, field, etc. - Common Well Names standardized by teams - Elevation reference can only be GR, KB, DF, SL or ES - County name must be valid County name - Deviation survey name wi... - Cycle time in loading data - Cycle time in finding, preparing, and moving data - Risk of loading errors - Risk of data becoming lost or unknown - Data duplication - Manual intervention for all data manag...
...to spatial data inaccuracy. For example, it is well documented and understood that datum conversion errors can result in positional ...errors of between 10 and 20 feet (Heggelund, 2008). But a quick calculation based on known average porosit...default values for International instead of U.S. feet and these differences can lead to positioning errors for wells and seismic navigation of up to 50 feet in the southern United States. ...
...orizon grids. The importance of 3-dimensional visualization and display was highlighted to discover errors introduced by export and import between applications. Anadarko also required a user friendly front ...
Copyright 2008, Society of Petroleum Engineers This paper was prepared for presentation at the 2008 SPE Annual Technical Conference and Exhibition held in Denver, Colorado, USA, 21-24 September 2008. This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright. Abstract Spatial attributes are pervasive in petroleum engineering data. Solutions that create a competitive advantage by effectively using this data require a sustainable architecture with documented workflows to increase user confidence and alleviate engineering risk. The components of a sustainable spatial architecture are; data accessibility, a standard data model, portal consumption and Geographic Information System (GIS) access, and data maintenance and business rules. A full lifecycle solution for management of spatial attributes includes collection from field and public domain sources, quality assessment and control, storage in standardized data models, distribution to analysis applications, and capture in knowledge management and audit systems. Each component and lifecycle stage can impact financial performance of the organization using the geotechnical data. Site assessments and a standard methodology for documentation of processes and components were used to compare solutions and value statements for multiple domestic and international operators. The assessments found that accessibility by end-users impacts quality, accuracy, and confidence related to spatial data. The economic impact of this component is lack of end-user confidence in data tools, and the cost of re-acquiring data. The data model provides feature class intelligence, naming conventions, and attribute accessibility, a standard taxonomy, and a method to move petroleum engineering data into a world of points, lines and polygons. The utility of this component is measured in lost opportunity costs of incomplete analysis, and inconsistent data causing poor drilling decisions. Portal access through service oriented architectures delivers visual and automated quality control of multiple data sources and is documented to save engineering time spent on data discovery and manipulation.
- Asia > Middle East (1.00)
- North America > United States > Texas > Harris County > Houston (0.28)
- North America > United States > Colorado > Denver County > Denver (0.24)
- Asia > Middle East > Kuwait > Jahra Governorate > Arabian Basin > Widyan Basin > North Kuwait Jurassic (NKJ) Fields > Marrat Formation > Upper Marrat Formation (0.98)
- Asia > Middle East > Kuwait > Jahra Governorate > Arabian Basin > Widyan Basin > North Kuwait Jurassic (NKJ) Fields > Marrat Formation > Sargelu Formation (0.98)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Knowledge management (1.00)
- Production and Well Operations > Well & Reservoir Surveillance and Monitoring > Production data management (0.95)
- Management > Strategic Planning and Management > Project management (0.94)
The Implementation of the Largest Geological and Production Corporate Database in Ukraine to Accelerate Digital Transformation
Reshetniak, Vadym (UkrGasVydobuvannya JSC UGV) | Akono, Arsene (Schlumberger) | Sherif, Rana (Schlumberger) | Boumehdi, Amine (Schlumberger) | Morsli, Sid Ahmed (Schlumberger) | Popko, Yaroslav (Schlumberger) | Yusupov, Diliaver (UkrGasVydobuvannya JSC UGV) | Zharkeshov, Sanzhar (UkrGasVydobuvannya JSC UGV) | Valetnyn Loktiev, Valetnyn (UkrGasVydobuvannya JSC UGV)
...or further analysis. That is why well files were scanned and brought to order by the chronology and reference to the field or area. The scope of digitised well cases exceeded 1 million ...pages. All well files, which were received and transmitted, went through data quality control for data co...
...taging area of the database, where automatic validity and consistency checks are performed, and any errors are flagged. The data manager can then perform quality control and validation based on the status o...ty …etc. For example, data entries for certain attributes are checked against a pre-defined list of reference values in a data glossary. These values are interpellated from the business management standards...
Abstract Two years ago, geoscientists of the leading East European gas producers were still using paper logs, unknown data quality sources, and many data versions, stored on individual local disks for their interpretation jobs. To overcome this challenge, the deployment of a geological and production database was initiated in the framework of the digital transformation programme. The key objectives were to build a single data repository for all company assets and integrate it with the production and drilling business systems. The development of the corporate data repository started with an extensive data assessment. A report of available data types, business processes, recommended data management, and business rules were produced. Loading and quality control procedures were designed to load over 40 different data types, including geology, geophysics, production, and drilling. Standardisation of data available in non-industry formats was necessary, e.g. for Lithology data. To enable reporting of drilling and production data stored in the business systems, complex integration and synchronisation between different Database Management Systems were developed. Data delivery to petrotechnical applications was a key to productivity. By implementing this centralised and unique Corporate Data Storage, digitalization and loading of the well, log, seismic, drilling, and production data with proper quality were enabled. Petrotechnical experts can now use one data access point to retrieve data into their applications quickly and efficiently using just an integrated web browser. Searching information within SEGY or DLIS files was previously a difficult process that has been facilitated through an application user interface displayed in the local language. Thousands of well logs, documents, and reports have been digitised and made available in the system. The interpretation results and knowledge are now captured and reused in future field development planning. All the company data including drilling and production data synched from business systems are now available in a single place and accurate reporting can be facilitated. The system allowed the reduction of the time spent by the users searching and data quality checks.
- Reservoir Description and Dynamics > Formation Evaluation & Management > Open hole/cased hole log analysis (1.00)
- Production and Well Operations > Well & Reservoir Surveillance and Monitoring > Production data management (1.00)
- Data Science & Engineering Analytics > Information Management and Systems (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (0.88)
- Information Technology > Information Management (1.00)
- Information Technology > Data Science > Data Quality (0.90)
Transfer Learning with Recurrent Neural Networks for Long-term Production Forecasting in Unconventional Reservoirs
Razak, Syamil Mohd (University of Southern California) | Cornelio, Jodel (University of Southern California) | Cho, Young (University of Southern California) | Liu, Hui-Hai (Aramco Americas) | Vaidya, Ravimadhav (Aramco Americas) | Jafarpour, Behnam (University of Southern California)
...8 URTeC-5687 Figure 4. Error histogram and samples of forecast versus simulated reference. The forecast for each coordinate in the ...reference map is obtained using the trained forecast model. The error histogram (between each forecast and si...mulated data using tuples extracted from the reference maps) and representative samples of forecast are shown in Figure. For the random scenario, the trai...
...our, B. (2020b). History matching with generative adversarial networks. In ECMOR XVII, volume 2020, pages 1-17. European Association of Geoscientists & Engineers. Mohd Razak, S. and Jafarpour, B. (2020c). ...arning Applications and Trends: Algorithms, Methods and Techniques - 2 Volumes. Information Science Reference - Imprint of: IGI Publishing, Hershey, PA. Pitman, J. K., Price, L. C., and LeFever, J. A. (2001). ...
... 2) are randomly selected, and the corresponding tuples of ((, , () a re extracted from the reference maps in Figure. A set of 150 production data (with 12 timesteps) is obtained using Equation 10 and ...
Abstract Robust production forecasting allows for optimal resource recovery through efficient field management strategies. In hydraulically fractured unconventional reservoirs, the physics of fluid flow and transport processes is not well understood and the presence of, and transitions between multiple flow regimes further complicate forecasting. An important goal for field operators is to obtain a fast and reliable forecast with minimal historical production data. The abundance of wells drilled in fractured tight formations and continuous data acquisition effort motivate the use of data-driven forecast methods. However, traditional data-driven forecast methods require sufficient training data from an extended period of production for any target well and may have limited practical use. In this paper, a deep recurrent neural network (RNN) model is developed for robust long-term production forecasting in unconventional reservoirs. As input data, the model takes completion parameters, formation and fluid properties, operating controls, and early (i.e., 3-6 months) production response data. The model is trained on a collection of historical production data across multiple flow regimes, control settings, and the corresponding well properties from multiple shale plays. The proposed RNN model can predict oil, water, and gas production as multivariate time-series under varying operating controls. Once the forecast model is trained, it can be used to obtain a one-step forecast by feeding the model with input well properties, operating controls, and a short initial production. The long-term forecast is obtained by either recursively feeding the model with forecast results from the preceding timesteps or by training the model for multi-step ahead predictions. Unlike other applications of RNN that require a long history of production data for training, our model employs transfer learning by combining early production data from the target well with the long-term dynamics captured from historical production data in other wells. We illustrate our approach using synthetic datasets and a case study from Bakken Play in North Dakota.
- North America > United States > North Dakota (1.00)
- North America > Canada (0.96)
- North America > United States > West Virginia > Appalachian Basin > Marcellus Shale Formation (0.99)
- North America > United States > Virginia > Appalachian Basin > Marcellus Shale Formation (0.99)
- North America > United States > Texas > West Gulf Coast Tertiary Basin > Eagle Ford Shale Formation (0.99)
- (13 more...)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Production forecasting (1.00)
- Production and Well Operations > Well & Reservoir Surveillance and Monitoring > Production data management (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Neural networks (1.00)
...not been reviewed by the Society of Petroleum Engineers and are subject to government via internet pages. In addition to production data, correction by the author(s). The material, as presented, does not...
..., averaged and displayed to the operator via an The benefits from this stage have been significant. Errors electronic interface. Utilizing this interface the operator can which can be quickly corrected by t...aves significant work at the head office at the end of the month when the allocations are balanced. Errors which used to take days to find and correct in the office are fixed at the operator interface. Prod...the report. The validation process is - JV splits of production rerun at this stage. Any validation errors must be corrected - Oil in storage splits by the operator before the report can be submitted. The ...
... middle tier. Real-time information is stored into a documents, reports, trends, etc which match or reference that real-time historian. The historian connects to several control word. The search engine's sourc...
Abstract Apache Energy has implemented a system which provides data from real-time well head process data to continuous month to date production information available to head office and joint venture partners over the internet. Apache is one of the largest gas produces in Australia with assets in Australia and overseas. There are several different Joint Ventures (JVs) involved in the various fields and processing plant. The operations are spread between Offshore, Varanus Island, Perth and Houston. Joint venture partners are located in Adelaide, US, Perth and Kuwait and gas customers are located throughout Western Australia. Such diversity requires effective information distribution. Over the last 5 years, Apache has implemented a process information management system, which enables production data to be transmitted securely all over the world, minutes after the end of each production day. Production data business flow commences with Apache's customers placing orders or "nominations" via the web based gas nominations system. The nominations are validated against the customer contracts, collated and the total gas production for the day is electronically issued to the operator. At the end of the gas day, production, well, and facility performance data is collected automatically and presented to the operator for validation. Once validated by operations, the data is loaded into the Production Reporting System (PRS) database and a series of applications are executed such as;Well allocation Sales gas reconciliation Joint Venture partner splits Environmental calculations The data is then released to the Apache users and the Joint Venture partners shortly after the end of the 08:00 production day. The data is also released on to the Internet under a security system which allows Joint Venture partners to view production, inventory/lifting and monetary splits between the JVs for the month. Environmental reports are issued to the government via internet pages. In addition to production data, the system provides engineers and management with remote adhoc real-time process data. Use of this system has enabled the engineering groups to identify well problems resulting in production benefits. Apache has been able to significantly reduce the amount of time required to provide JV reports and JV payments and double handling of data has been virtually eliminated. The goal of entering data once, at the source, has largely been met. This paper will address the processes and business benefits Apache have observed from this knowledge management system. The Apache Assets The main Apache assets addressed by this paper are located off the North West of Australia. Oil, condensate and gas are produced from several offshore facilities. These consist of manned production facilities and unmanned monopods. Production is separated in these offshore facilities and the hydrocarbons pumped to the main processing facilities on Varanus Island. Several of the wells are piped directly to the island.
- Oceania > Australia > Western Australia (0.34)
- Asia > Middle East > Kuwait (0.25)
- Production and Well Operations > Well & Reservoir Surveillance and Monitoring > Production data management (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Knowledge management (0.90)
- Information Technology > Knowledge Management (1.00)
- Information Technology > Information Management (1.00)
- Information Technology > Architecture > Real Time Systems (0.71)
- Information Technology > Communications > Networks (0.70)
...2009; Jafarpour and Tarrahi, 2011]. While the filter has well known limitations, including sampling errors and sub-optimality for nonlinear non-Gaussian problems, it is recognized as a practical estimation ...
...e summarized as (see Appendix for notation): (4) (5) However, due to finite ensemble size, sampling errors are introduced that can lead to unphysical (spurious) correlations. Consequently, the spurious corr...
...length (x-axis) and fracture conductivity (y-axis). The intersection of the two axes represents the reference values for each parameter. The initial values (before data assimilation) are scattered around the t...ues are plotted with different markers. The final ensemble is concentrated around the intersection (reference fracture permeability and half-length). However, the updated fracture half-length covers a wider ra...e 4, first row). While in the majority of realizations, the estimated values are moved close to the reference fracture parameters, in some realizations a combination of updated fracture permeability and (in pa...
Abstract Hydraulic fracturing is performed to enable production from low-permeability and organic-rich shale oil and gas reservoirs by stimulating the rock and increasing its permeability. Characterization and imaging of hydraulically induced fractures is a critical step in enhancing production predictions and the stimulated reservoir volume (SRV). Recorded tracer concentrations during flowback and historical production data can reveal important information about fracture properties, including geometry and hydraulic conductivity. However, the complexity and uncertainty in fracture and reservoir descriptions, coupled with data limitations, complicate the estimation of fracture geometric and hydraulic properties. In this paper, tracer test monitoring and production data are used to infer fracture half-length and hydraulic conductivity in planar fracture models, using the ensemble Kalman filter (EnKF), a well-established data assimilation method. Prior to data assimilation, the sensitivity of tracer test and production data to fracture half-length and hydraulic conductivity is investigated. For data assimilation, an ensemble-based probabilistic description represents fracture half-length and conductivity for each stage, with significant initial uncertainty. At each update step, tracer test and production data are assimilated to reduce the parameter uncertainty. The results indicate that tracer test and production data have a complementary role in estimating fracture half-length and conductivity, with the former being more sensitive to hydraulic conductivity while the latter is more affected by the fracture half-length. Numerical examples are presented to evaluate the EnKF performance and to investigate the sensitivity of tracer and production data to fracture length and conductivity.
...ere significantly reduced. Automation of data loading and quality check has decreased the number of errors related to the human factor. Specialists have acquired a convenient tool for collecting, storing an...
...sured or estimated figures, i.e. measured data are not equal to the tank data, which are counted as reference and the measured data are corrected by allocation coefficient. Further they are formed into monthly...
...created by plugin. Subsequently, all the engineer should do is just to quality check and in case of errors to correct them. Initially, many ...errors related to program settings shortages and simple mistakes of data importing happened but now we fac...e less and less errors due to gained experience and knowledge. Nowadays, only one personnel works on it and, usually, by 1...
Abstract Integrated production data management system (PDMS) solution is presented that incorporates three remote locations of the company's different departments (Field Operation, Engineering and Economics) into a single working environment. Automated workflows implemented on the basis of PDMS aimed to improve speed and efficiency of engineering analysis and economic planning. PetroKazakhstan Kumkol Resources JSC operates a wellstock consisting of more than fifteen hundred wells. Operational data is loaded into a system on a daily basis at a central office on the field site. At this stage data is quality checked and used for production monitoring. All information is automatically synchronized to the office in Kyzylorda city, where production back allocation is performed. Every month regulatory reports are generated from the system. Decline curve analysis forecasts at well level are frequently applied for daily routine economical assessment of well intervention and surface construction operations. Moreover, saved forecasts are also automatically available for budgeting purposes in Almaty office. As a result of integrated PDMS solution, PKKR JSC engineers and economists get easy and automated access to raw operational data as well as results of allocated production and forecasting by the decline curve analysis (DCA). Automated workflows for back allocation, regulatory report generation and forecast result data sharing made different departments closer and decision making more efficient. Overall, data travel distance of about 1500 km. Shared workspace with standard analytical templates (plots, reports, maps, forecasts) allowed engineers to promote best practices across organization, align reservoir surveillance and monitoring approach in a common standardized way. PDMS allows engineers, previously overloaded with manual data handling and reporting, to have more time to solve reservoir and production engineering problems. Meanwhile, the economists gain direct and automated access for DCA forecasts, performed by reservoir engineers, not only at field, but also at well level. This helps prioritize each well's economic potential and rank accordingly, which is critical in budget planning. The novelty of integrated PDMS solution is that it brings a new level of integration of different departments into synergy. Teamwork spread beyond engineering, operations and geology, adding economists into the team to address today's operational challenges.
- North America > United States (1.00)
- Asia > Kazakhstan > Almaty Region > Almaty (0.25)
- Asia > Kazakhstan > Kyzylorda Region > Kyzylorda (0.24)
- Asia > Middle East > Iraq > Dhi Qar Governorate > Arabian Basin > Widyan Basin > Mesopotamian Basin > Garraf Field (0.99)
- Asia > Kazakhstan > Kyzylorda Oblast > Turgai Basin > Kumkol Field (0.99)
- Reservoir Description and Dynamics > Improved and Enhanced Recovery > Waterflooding (1.00)
- Production and Well Operations > Well Operations and Optimization (1.00)
- Production and Well Operations > Well & Reservoir Surveillance and Monitoring > Production data management (1.00)
- Data Science & Engineering Analytics > Information Management and Systems (1.00)
An Integrated and Automated Workflow to Enhance Production Efficiency and Compliance in a Giant Offshore Field
Zhao, Wenyang (ADNOC Offshore) | Al Qubaisi, Salama Darwish (ADNOC Offshore) | Al Kindi, Salem Ali (ADNOC Offshore) | Al-Feky, Mohamed Helmy (ADNOC Offshore) | Al Shehhi, Omar Yousef (ADNOC Offshore) | Sugawara, Yuki (ADNOC Offshore)
...gnificant benefits, including increasing productivity, eliminating redundant manual tasks, reducing errors, increasing transparency and improving work culture. Methodology The approach is built based on t...
... be fetched automatically into the production compliance reporting tool and be utilized as the main reference. Any changes to the allowable can be updated in order to avoid misinformation. Daily allocation is ...
...us section, a dashboard is included in the violation reporting tool. The dashboard consists of four pages. The first page displays the high level summary of the field violation status during the selected p...
Abstract Daily production compliance is fundamental to sustain reservoir management excellence and ultimately achieve an optimum oil recovery. The production activities execution is critical to adhere to the reservoir management guidelines and best practices. It is a more challenging task in brownfields due to the limitation of controlling system and limited access especially in offshore fields. A timely and efficient approach is undoubtedly necessary to enhance production efficiency and compliance. An integrated and automated tool has been innovated to analyze and report well production status against the guidelines and requirements in a mature offshore field with more than 50 years history. This systematic approach has been developed through integrating the planned rate, daily actual production rate, latest flow tests, and current well performance. Noncompliance is reported automatically on a user defined time scale, including daily, weekly, monthly or any customized time range within the month time. Daily violation report is generated automatically and sent to production operation for prompt adjustments and other requested actions. The automated workflow enables both daily production reporting and production compliance reporting. Daily production reporting is a routine work, which usually takes a lot of time every day. The workflow is capable of reducing 90% of the time comparing to the manual way. Production compliance reporting is currently mainly focusing on the comparison of actual production to planned rate and guideline rate. Any exception will be reported as violation. The violation dashboard summarizes the details based on the user selected time range. On daily basis, an email containing the violation details could be generated and sent to the corresponding teams for corrective actions. In this giant brown field, production GOR is a primary controlling parameter. The latest flow tests have been taken into account to evaluate the gas production compliance. Any violation to the GOR guidelines will be reported in the same communication email for timely correction. With the innovated tool, the violation ratio of the giant offshore field has been successfully reduced and controlled. The usual responding time for corrections has been dramatically reduced from months to days.
- Europe (0.52)
- Asia > Middle East > UAE > Abu Dhabi Emirate > Abu Dhabi (0.17)
- Production and Well Operations > Well Operations and Optimization (1.00)
- Production and Well Operations > Well & Reservoir Surveillance and Monitoring > Production data management (1.00)
- Data Science & Engineering Analytics > Information Management and Systems (1.00)
...cies. As part of Hazard and Effects Management Process (HEMP), various hazards were identified with reference to the ISO 17776:2000 and bow tie was prepared for Major Accident Hazards. As part of HEMP, the HSE...
...ay to information sharing both within and across the business. It holds general information and key reference documents that are of general use. The key benefits of having a business portal are: Better decisio...
... requirement. MIS reports are generated automatically with minimum manual data entry to avoid human errors and saving valuable time of personnel. Fig. 22 - Management Information System (MIS) - DPR Automat...
Abstract Every once in a while there comes a game changer, sometimes through serendipity and sometimes through grit and resolve. Here is a project that showcases how grit, resolve and world class management can impact an entire nation’s destiny. The discovery and subsequent development of the deepwater oil and gas fields, in the KG-D6 Block off the Bay of Bengal in the east coast of India, have important ramifications for the country’s energy security. And the discovery and development project has been executed by India’s largest corporate house, Reliance Industries Ltd (RIL). As Oil and Gas companies prepare for a surge in worldwide consumption, they must focus on Implementation of Best Practices in terms of Effective Systems and Processes to realize Operational Excellence. It translates the vision into set of business operational & measurable objectives. Today Oil and Gas Industry is having challenges like Asset Integrity, Reliability, Competency, Environmental regualtions and compliances. Inspite of these challenges, to deliver on incident free operations and production availability; oil and gas organizations are seeking industry-specific solutions. These solutions must support standards of excellence that help the organizations to achieve compliance, safety and reliability goals; improve the planning process; enable continuous improvement; and enhance organizational learning. Information is the fuel that allows leading organizations to make data-driven decisions. Effective Database Management and Information Flow are of key importance to enable organizations to continue the safe, efficient, effective and responsive operations of oil and gas business. These are the effective tools that help oil and gas organizations to achieve excellence in the areas of Asset Performance Management, Risk Management, Asset Integrity Management, Competency Management, etc. Reliance Industries Ltd. (RIL) realizes and fully acknowledges this importance of effective processes and sytems as one of the critical factors for the success of its oil and gas business. RIL has initiated the challenging initiative of implementing the best practices on systems and practices at the first ever deep water field of India – KGD6. These are strong foundation for the operataions at KGD6. Excellent data management has changed the dynamics of efficient business management during all the key stages of lifecycle of this prestigious asset – Project Development, Execution, Precommissioning, Commissioning and Steady State Operations. RIL has developed and established robust data management systems, supporting tools, systems and processes that are acknowledged as best practices across the Oil and Gas industry. Some of the best practices implemented are Bow-tie Analysis for Risk Management and development of HSE Case, Production Data Management System – Integrated with real time data from Distributed Control System, Compliance Management System through dedicated Intra-net based portal, Plant Change Management System through SAP platform, Document Management System with global search features and Competency Management System. These have not only enabled the organization to achieve flawless start-up of India’s first deep water field but also enabled to achieve Excellence-in-Excellence as recognized through various accrediations and third party International aclaimed audits. Through this paper, the authors explain the efforts taken by RIL in E&P operations to establish best industry practices and its benefits to the organization in terms of tangibles and intangibles. Some of these are mentioned as below:- ▪HSE Management System developed and well established in line with Shell Global Solutions and latest techniques of Bow-tie analysis, Tripod analysis is in place which ensures LTI Free Operations (Zero LTI). ▪Reliance Safety Observation Process (RESOP) is implemented as an effective tool for improving Behavioural Safety of own employees and contract employees. ▪Round the clock Decision Support system for Control Room Engineers through Production Simulation System (PSS), supplied by Kongsberg; Norway. PSS is integrated with Distributed Control System (DCS), which results in Flawless Operations and also effective Production Planning. ▪Production Data Management System (PDMS) – Excellent operating data management and its availability even at remote locations and corporate office on a 24×7 basis, which is a back bone for analysis and interpretation by internal experts for making corrective actions at every stage of Operations. Production Data Management System (PDMS) supplied by Halliburton is integrated with real time data from DCS through Aspentech Data Historian interface. ▪Compliance management through intra-net based portal, developed by Price Waterhouse Coopers (PWC) for effective monitoring of all regulatory compliances through a single platform, resulting in 100% compliance. ▪SAP ERP Solutions – Plant Maintenance module for effective integration, planning and mangement of resources which ensures 100% system availability. ▪Laboratory Information Management System (LIMS) supplied by LabVantage - All laboratory analsysis and results are available with trends on intranet, which enables making corrective actions immediately. ▪REIMS (Reliance Enterprise Information Management System) is established which ensures 100% availability of Documents with global search features. About 1,00,000 documents of KGD6 are presently available on REIMS. ▪Operator Training Simulator (OTS) supplied by Kongsberg, which ensures well trained and competant engineers are deployed for control room operations. ▪Process simulation packages like Aspen Plus, Hysys, PHAST, etc. are in place to enable detailed process studies for implementing plant modifications following Management of Change system through SAP platform.
- Asia > India > Andhra Pradesh > Bay of Bengal > Krishna-Godavari Basin > Block KGD6 > KG-D6 Field (0.99)
- Asia > India > Andhra Pradesh > Bay of Bengal > Krishna-Godavari Basin > Block KG-DWN-98/2 > KG-D6 Field (0.99)
- Production and Well Operations > Well & Reservoir Surveillance and Monitoring > Production data management (1.00)
- Management (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Knowledge management (1.00)