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Although leak incidents continue, a pipeline remains the most reliable mode of transportation within the oil and gas industry. It becomes even more important today because the projection for new pipelines is expected to increase by 1 billion barrels of oil equivalent (BOE) through 2035. In addition, increasing the number and length of subsea tiebacks faces new challenges in terms of data acquisition, monitoring, analysis, and remedial actions. Passive leak-detection methods commonly used in the industry have been successful with some limitations, in that they often cannot detect small leaks and seeps. In addition to a thorough review of related topics, this study investigates how to create a framework for a smart pigging technique for pipeline leak detection as an active leak-detection method.
Numerical modeling of smart pigging for leak detection requires two crucial components: detailed mathematical descriptions for fluid-solid and solid-solid interactions around pig and network modeling for the calculation of pressure and rate along the pipeline using iterative algorithms. The first step of this study is to build a numerical model that shows the motion of a pig along the pipeline with no leak (i.e., at a given injection rate, a pig first accelerates until it reaches its terminal velocity, beyond which the pig moves at a constant velocity). The second step is to construct a network model that consists of two pipeline segments (one upstream and the other downstream of the leak location) through which the pig travels and at the junction of which fluid leak occurs. By putting these multiple mechanisms together and using resulting pressure signatures, this study presents a new method to predict the location and size of a leak in the pipeline.
Multilateral wells are a popular choice for fields where maximum fixed asset utilization is required. Although Drilling and Intervention (Stimulation) techniques for such wells are mature and reliable, reservoir surveillance required novel method for lateral access. Such technology was developed and used in more than 40 wells in Middle East. Summary of experience, description of tools and methods, lessons learned and vision for further development will be described in this work.
In order to be useful for lateral reentry and logging, intervention system should comply with following minimum requirements: Be compatible with logging tools and systems provided by various Service companies and with various conveyance methods. Operate with logging systems, not equipped with wired through capability. Be of slim design so it can be used in variety of completions. Be equipped with diagnostics systems to detect laterals and confirm successful entry.
Be compatible with logging tools and systems provided by various Service companies and with various conveyance methods.
Operate with logging systems, not equipped with wired through capability.
Be of slim design so it can be used in variety of completions.
Be equipped with diagnostics systems to detect laterals and confirm successful entry.
All these requirements were implemented in new Multilateral reentry system. Once in field, system was proven reliable and useful for logging intervention in multilateral wells in various operating conditions.
Multilateral reentry system, consisting of motorized bend sub, wireless communication bridge and diagnostic section was designed, tested and further deployed in field. Within 40 jobs performed it was operating in variety of environments (onshore, offshore); open hole and cased hole completions; was run in oil, gas and water wells, in conjunction with most of commonly used logging systems. Although during most of jobs conveyance was performed using electric line Coiled Tubing, some of wells were accessed on wireline using Well Tractor technology. The system was proven reliable and practical experience was gathered for successful operation of this and any other multilateral reentry system to be designed in future. Application of this system allowed to receive important data needed for fields development and allowed optimization of completion systems in some cases. Although overall application of multilateral intervention system was a success, improvement areas were identified in order to increase operational portfolio; and will be presented in this paper. Recommendations of intervention friendly well design will be shared as well.
As unique experience of running Multilateral reentry system for logging purpose is described here, with practical recommendations on well construction, surveillance planning, execution and evaluation, this paper will be interesting to wide category of practicing engineers of various specialties.
Maus, Stefan (H&P Technologies) | Gee, Timothy (H&P Technologies) | Mitkus, Alexander M. (H&P Technologies) | McCarthy, Kenneth (H&P Technologies) | Charney, Eric (H&P Technologies) | Ferro, Aida (H&P Technologies) | Liu, Qianlong (H&P Technologies) | Lightfoot, Jackson (H&P Technologies) | Reynerson, Paul (H&P Technologies) | Velozzi, David M. (H&P Technologies) | Mottahedeh, Rocky (United Oil & Gas Consulting Ltd)
Development of autonomous drilling technologies requires the automated analysis and interpretation of Logging While Drilling (LWD) data to optimally land the well in the target formation and keep it in the pay zone. This paper presents a fully automated geosteering algorithm, which includes advanced LWD filtering, fault detection, correlation, tracking of multiple interpretations with associated probabilities and visualization using novel stratigraphic misfit heatmaps.
Traditional geosteering uses manual stretch, compress and match techniques to correlate measurements along the subject wellbore against corresponding reference type logs. This results in a crude representation of strata by linear sections with offsets at fault locations. Instead of automating this manual process, we instead determine the possible interpretations as solutions of a geophysical inverse problem in which the total misfit between the subject and reference data is minimized. Interpretations are parameterized as discontinuous splines to accurately follow curved strata interjected by fault offsets. To account for ambiguities, multiple possible interpretations are continuously tracked in real time and assigned probabilities based on the misfit between the latest measurements and the reference data. Unrealistic solutions are suppressed by penalizing strong curvature and large fault offsets. Viable interpretations are simultaneously visualized in real time as paths on a novel stratigraphic misfit heat map, where they may be corroborated against valleys of minimal misfit between the subject and reference data. The user can guide the interpretation by setting control points on the heat map which the automated solutions must respect.
The algorithm has been validated using wells from different regions across North America for which previous manual geosteering interpretations are available. The automated spline interpretations represent the actual curved strata more accurately than manual interpretations. Operationally, the automated interpretations can be provided within minutes compared to typical manual turn-around times of hours. Automation leads to more consistent and repeatable results, removing the subjectivity of manual interpretations.
Risk analysis is a term used in many industries, often loosely, but we shall be precise. By risk analysis, we mean applying analytical tools to identify, describe, quantify, and explain uncertainty and its consequences for petroleum industry projects. Typically, there is money involved. Always, we are trying to estimate something of value or cost. Sometimes, but not always, we are trying to choose between competing courses of action. The tools we use depend on the nature of the problem we are trying to solve. Often when we are choosing between competing alternatives, we turn toward decision trees.
Jeeves, Simon Clausen (MRC Global Norway as) | Berstad, Carina (University in Bergen Student) | Gjøvåg, Markus Bratten (University in Bergen Student) | Riple, Mathias (University in Bergen Student) | Bjorøy, Marita (University in Bergen Student) | Sangolt, John-Kenneth (University in Bergen Student) | Erstad, Thomas (University in Bergen Student)
Valves are critical and essential parts in an oil & gas (O&G) production plant, which means that if a failure occurs it can potentially great implications on safety and profitability of the plant.
The traditional approach in today's oil and gas process industry for verifying valve reliability is simplistic and provides little information regarding valve degradation mechanisms.
Therefore, this paper discusses new theories and concepts to help increase the reliability of production & safety critical valves.
Focus has historically been targeting on using corrective and scheduled maintenance strategies, rather than using condition and performance monitoring applications as a tool to plan and prioritize maintenance and achieve a higher standard for safety.
In the search for a solution to increase the reliability of production and safety critical valves it is reasonable also to look at industry 4.0 and its concepts. The digital revolution which implements concepts such as cyber-physical systems (CPS), industrial internet of things (IIoT) and cloud-based storage. Based on these concepts the paper visits the use of valve diagnostic systems for identifying failure mechanisms of valves subjected to degradation by the application of sensors and small computers used to continuously monitor valve performance and store this data within cloud-based servers.
Today we experience an enormous progress in real time monitoring by use of sensors in conjunction with data collectors. There is new technology to process and gather data that supports decision making, such as Valve Diagnostic System (VDS). By taking advantage of new technology there are immense benefits to be had by monitoring valves rather than traditional physical observation and testing. This method has multiple advantages and it can contribute to reducing maintenance cost, down time and increase safety.
Multiple unwanted and hazardous events have occurred through the last decades, such as the tragic Piper Alpha disaster (Macleod & Richardson, 2018). This has contributed to drive the industry to improve and adhere to safety regulations and requirements. This makes the sensor technology even more beneficial and opens a wide range of potentials within the maintenance- and risk management genres.
Today all in-operation risk calculations are based on reported accidents or unwanted occurrences. This practice gives way to inaccurate reliability calculations that are indisputable and inaccurate. It is a practice where potentially many failures are not reported, understood or even revealed. Experts within Functional Safety Management points to the basis of the calculations with skepticism due to the validity of the data and the fact that the data in itself is not a proactive approach to sound valve management.
The trend towards digitalization is becoming stronger and disruptive. Thanks to the experience gained over the past two decades, rotating equipment OEMs are now able to connect assets from any location, transfer data with cyber secure protocols, run analytics on the fly and manage remotely alerts. The above helps to provide valuable insights to customers, partner with them and drive high availability and reliability, optimize operations and support maintenance decisions (Allegorico, 2014). This paper addresses the problem of assessing the health status of a Dry Low Emissions (DLE) combustion system, one of the most critical components of a Gas Turbine. It describes how the combined use of remotely-acquired operational data and different types of analytics, which represents a digital replica of the system, is used in conjunction with expert's domain knowledge to drive planned and unplanned maintenance decisions. We applied this strategy to an Oil&Gas plant and the results of the integrated service delivered have been observed for several months, providing feedback on the methodology as well as points of reflection for further enhancements.
Pipelines are the most economically viable mode of transportation for oil and gas. Every pipeline is monitored 24×7 using meters distributed across the pipeline. Flow, temperature and pressure meters are the most common and essential for continuous and efficient operation of pipelines. Like any other instrument these meters also have uncertainty and prone to error due to irregular calibration, drift, gross error and other such events. The overall accuracy of pipeline metering increases as the distance between consecutive meters decreases. It is also affected by the placement of meters at critical locations like pipeline tapouts, tapins and consumers points. Economics do not allow pipeline operators to install beyond a certain amount of metering assets.
The complexity to efficiently calculate the product in and out of a gas pipeline is more compared to a liquid pipeline. It arises due to the high compressibility of gases compared to liquids. Gas pipelines operate at much higher pressure than oil pipelines. The trapped gas inside a gas pipeline can be called line pack of that pipeline. The line pack is very sensitive to two natural factors pressure and temperature of the pipeline. Oil pipelines carry one fluid at a time. Gas pipelines on the other hand carry several gases as a mixture. Unlike oil, gas billings are calculated as the energy the gas mixture carries to the consumer. Due to the mixture, gas composition is another essential factor to accurately calculate energy of the mixture.
This paper discusses the challenges of calculating various transport factors and phenomena in gas pipelines and how methods like gross error correction and machine learning can be used to increase the accuracy. The results and conclusions are derived from the applications of these methods to natural gas transportation pipeline. Some of most important conclusions obtained were Understanding the pattern of on-field meter data with ideal meter provides insights in the root cause of the problem. e.g. sudden spike in temperature leading to error in line pack. Creating digital twin of all metering assets allows faster isolation of pipeline sections having calculation errors. e.g. by monitoring the difference between field and ideal parameters. Having a central meter diagnostics system that combines the data from meters of different make and models improve the pattern recognition and error detection ability. Gross error detection isolates the meters inducing error. The feedback can be provided to the machine learning algorithms for root cause analysis.
Understanding the pattern of on-field meter data with ideal meter provides insights in the root cause of the problem. e.g. sudden spike in temperature leading to error in line pack.
Creating digital twin of all metering assets allows faster isolation of pipeline sections having calculation errors. e.g. by monitoring the difference between field and ideal parameters.
Having a central meter diagnostics system that combines the data from meters of different make and models improve the pattern recognition and error detection ability.
Gross error detection isolates the meters inducing error. The feedback can be provided to the machine learning algorithms for root cause analysis.
Note: This paper only covers the gross error of meters. There are methods used to reduce other meter errors namely random, limiting and systematic not covered in this paper. Readers are requested to read relevant material to understand the complete scope of errors in metering systems.
Thiberville, Caitlyn (Louisiana State University) | Wang, Yanfang (Louisiana State University) | Waltrich, Paulo (Louisiana State University) | Williams, Wesley (Louisiana State University) | Kam, Seung Ihl (Louisiana State University)
Although leak incidents continue, a pipeline remains the most reliable mode of transportation within the oil and gas industry. It becomes even more important today because the projection for new pipelines is expected to increase by 1 billion BOE through 2035. In addition, increasing number and length of subsea tiebacks face new challenges in term of data acquisition, monitoring, analysis, and remedial actions. Passive leak-detection methods commonly used in the industry have been successful with some limitations in that they often cannot detect small leaks and seeps. In addition to a thorough review of related topics, this study investigates how to create a framework for a smart pigging technique for pipeline leak detection, as an active leak detection method. Numerical modeling of smart pigging for leak detection requires two crucial components: detailed mathematical descriptions for fluid-solid and solid-solid interactions around pig, and network modeling for the calculation of pressure and rate along the pipeline using iterative algorithms. The first step of this study is to build a numerical model that shows the motion of a pig along the pipeline with no leak, i.e., at a given injection rate, a pig first accelerates until it reaches its terminal velocity, beyond which the pig moves at a constant velocity. The second step is to construct a network model that consists of two pipeline segments (one upstream and the other downstream of leak location) through which the pig travels and at the junction of which fluid leak occurs. By putting these multiple mechanisms together and using resulting pressure signatures, this study presents a new method to predict the location and size of a leak present in pipeline.
Nasreldin, Gaisoni (Schlumberger) | Gibrata, Muhammad A. (Dragon Oil) | Rajaiah, Nanthakumar (Schlumberger) | Elsadany, Karim (Schlumberger) | Subbiah, Surej Kumar (Schlumberger) | Mukherjee, Anubrati (Schlumberger) | Eid, Tarek (Dragon Oil) | Eldali, M. A. (Dragon Oil) | Skelhorn, Richard (Dragon Oil) | Rouis, Lamia (Dragon Oil) | Oweni, Tarek (Dragon Oil) | Knispel, Ricarda (Dragon Oil) | Aly, Omar (Schlumberger) | Yousif, Mohamed Baqer Al Asadi (Schlumberger)
The oil field stacked sandstone reservoirs of the South Caspian basin in Turkmenistan are currently undergoing further field development—with the addition of deviated wells. The localized depletion occurring in some of the offshore fields in this area has thus far triggered a host of geomechanics-related challenges—including wellbore instabilities and poor hole quality. In anticipation of further depletion over the remaining fields life, geomechanics effects will become more pronounced and the associated technical and economic challenges facing these fields may increase.
To assist in future well planning and field development, and to diagnose the problems already encountered in the existing vertical wells, 3D seismic-driven mechanical earth models (MEMs) were built. These covered the main sandstone reservoirs as well as the shaley formations. This integration of data from drilling operations, open hole logs, core, seismic and formation pressure measurements provided a constrained and consistent description of the prevailing in-situ state of stress, pore pressures and rock mechanical properties. These geomechanical models were further improved by accounting for historical depletion in the fields considered. The depletion modelling was performed numerically—using a simulator performing finite difference fluid-flow calculations. The results obtained and understanding gained were then considered in the analyses of wellbore stability for future wells.
This paper describes these geomechanical analyses and modelling—including the data integration to assess wellbore stability at the current level of depletion.
The yellow box and surface systems on the left are being used to generate an electromagnetic field thousands of feet above a horizontal well’s lateral section. When the fluid disturbs this field during a hydraulic fracturing operation, the delta is measured, allowing shale producers to see where the fluid is going. This year, the service did its first real-time trials. While waiting to give a presentation at an industry conference last year, Dale Logan was sitting in his chair listening to one of the speakers lined up ahead of him. The man at the podium was describing an emerging fracture diagnostics system that analyzes the “noise” coming out of a hydraulically stimulated well to measure the size of its fractures.