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 ±0.5°C for temperature measurements and ±0.05% 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 150°C 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.
In the high risk, highly technical and demanding environment of offshore oil and gas operations, there are very few items more critical to the safe and effective execution of the overall program than well control and isolation. Regarding well isolation, multiple barriers require the cementing job plan and the corresponding procedures be conducted as designed. In addition, the cement unit and supporting equipment need to be available and reliable during the job. Both are keys to the overall success of the cementing operation. With the realities of offshore cementing in mind, the industry wide challenge in finding appropriate talent in the respective regions further complicates the cementing service delivery. This type of situation requires a step change in the way cementing operations are currently done to drive the necessary process and quality assurance. Taking lessons learned from other industries with similar risk profiles, process and quality assurance can be achieved by providing: 1. A "second set of eyes" to drive the necessary process and quality assurance 2. Access to a trusted advisor that will back up remote resources during the critical junctures of the job By leveraging the latest in situational awareness and real-time technologies, offshore cementing will usher in a new age where the cementing specialist can access the knowledge and live assistance of experts across the world. This paper will discuss an approach and the benefits of enabling real-time remote advisory services for remote cementing operations.
Typical activities of the oil industry installations often involve risk situations that threaten the integrity of facilities, equipment, people or the environment. In this industry there have been many examples of incidents and accidents which unfortunately most of them with fatal consequences and all due to the lack of prevention in realization of activities which involving risk situations on the facilities.
It is for this reason that Petroleos Mexicanos (PEMEX) has implemented a Risky Activities Permission System (SPPTR for its acronym in spanish), which provides checks for the realization of activities on site through the classification of them in Class A (high risk, and printed in red paper) and Class B (low risk, and printed in blue paper).
According to SPPTR, these permissions are stored, followed and managed in the workplace in an area called Permission Control Center (or CCP for its Spanish acronym).
This project describes a solution based on ICT which support the management process of the Control Permission Center (CCP for its Spanish acronym). This solution allows you to track in detail the activities performed in the workplace indicating graphically in a plot plain the place in the facilities where are being performed these activities, the number of people involved, what company performs these activities, and if is necessary to perform other simultaneous activities, avoid simultaneous activities which involve some kind of risk, which contributes to strengthening security in the facility.
We obtained a solution which not only helps in the efficient management of permissions, it can even be a factor that prevents damage to facilities or equipment or environment or even, save lives of workers.
This paper explain this solution developed at Universidad Tecnológica de Campeche (UTCAM), which originally was focused to marine oil platforms, but could easily adapted for any kind of installation.
A major Big Data challenge facing the oil and gas industry is managing the high costs of data created, stored and analyzed. This issue is strongest in the upstream part of the industry due to data-rich operations such as seismic exploration, real-time drilling performance optimisation, integrated operations, and others. To help solve this problem, companies are increasingly using analytics and advanced communications technologies to better support business decisions for exploration, drilling and production, to help streamline processes, and to lower health safety and environmental (HSE) risks. A key enabler is the use of cloud computing to gather and manage the large volumes of sensor data for analysis, and a variety of structured and unstructured data in real time. Cloud computing solutions also enable new use cases and innovative applications to improve planning, forecasting, pricing optimization, and other complex processes. By providing powerful compute, storage, and application resources over public and private networks, cloud technologies open up new possibilities for oil & gas data delivery, processing and value generation. However some of the latency-sensitive, non-fixed location, and geographically coordinated applications, present a challenge for cloud-based solutions, particularly as emerging Internet of Things (IoTs) use cases require mobility support and geo-distribution in addition to location awareness and low latency. A new breed of IoT sensors in O&G Upstream, such as distributed acoustic sensing that is capable of generating data in excees of 1TB per day per well are putting more pressure on limited communication capabilities to transport the data from the remote edge to the cloud processing facilities. Thus a new platform is required to meet these requirements.
Drilling operations involve substantial planning and execution to achieve safe and cost effective well delivery. To improve drilling efficiency by minimizing costly non-productive time, active surveillance programs leverage large volumes of data generated at various stages of the drilling process. With increased reliance being placed on real-time quantitative measurements at the drill site, there has been recognition of the limited use of the large amount of valuable unstructured data generated throughout lifecycle of the workflow. Generated data in the form of communication messages and daily reports include substantial information and recordings of activities. However, such worthy sources of insight are largely untapped by conventional, established tools for monitoring and alert.
We present a proof of concept for mining daily drilling reports for three wells drilled between 2008 and 2010. There were occurrences of drilling impacts and NPT events ranging from slight operational delay to other events that introduced delays to the critical path. Employing concept extraction and pattern frequency techniques, we were able to track and monitor reported symptoms of the observed behavior to help identify root cause and compound factors leading to such an event.
In an effort to bring a fundamental understanding of the unstructured data relevant to the process, whilst simultaneously reducing the time required collating and processing this data, we applied unsupervised learning methods Results reveal associations between extracted patterns of some key issues and relative progress over a period of time which were not apparent to drilling engineers at first glance. In addition, the technology provides enhanced capability around the interpretation and visual representation of largely untapped collection of documents. The process can be automated to integrate pattern extraction and visualization with existing systems to empower engineering staff and technical specialists involved in the monitoring and analysis of well construction.
Optimization of waterflooding operations, whether reducing water cut (WC) or increasing ultimate oil recovery, has been a great challenge. With advancing technology, intelligent wells with controllable downhole chokes have provided the petroleum industry with efficient tools. Several techniques originating from different industries have been applied within the petroleum industry to address such challenges. This paper presents the advantages of using intelligent well valves compared to the base case of conventional wells with simultaneous use of a next-generation reservoir simulator and a user-friendly, robust optimization tool by maximizing net present value (NPV) and cumulative oil production.
Recovery has been enhanced using dynamic and smart control of interval control valves (ICVs) (
How Do You Start the Fire a Second Time?
Change management is a common theme within digital energy discussions. Although it is often suggested as a significant challenge, there are very few objective metrics for judging whether change is actually occurring. This means that judging the success of an initiative has been largely subjective.
One source of objective information is application usage. The implementation of digital energy principles brings changes to both the way applications are used and to the types of applications that are called. One component of a successful digital energy project is shifting work from manual tasks to automated work processes. In automated processes, workflows call applications to make necessary calculations instead of users checking out applications from a limited number of authorized licenses. An increase in workflow software usage can be tracked and used as one objective indicator of change. Other objective indicators are reduced application use by individuals and increased use of new and/or different applications and calculations such as analytics and statistical process control methods.
Therefore, once an organization establishes a baseline level of application and/or function usage for key workflow components then subsequent usage trends that resulted from a digital energy initiative can be monitored. For example, an individual working on an engineering design workflow may check out a license for an application for 30 minutes, acquire data, build a scenario and produce 30 calculation results in a scenario analysis. In an automated work process for the same design problem, the workflow itself calls the same application and utilizes it for 3 minutes while running 600 calculation results. By monitoring application usage, an assessment of the progress in switching to the new way of working can be monitored. In this example, the process time has decreased by a factor of 10 while the engineering rigor has increased by a factor of 20. If the statistics continue to show automated workflows in use as opposed to individual usage, it can be inferred that a change in work habits has taken place.
Although no qualitative information on decision quality is produced by application tracking, quantitatively, more wells can be analyzed and the chances of arriving at a better decision are greatly increased. Application usage data may not be a definitive indicator of change. However, when objective data is combined with qualitative feedback and other subjective data points, a more complete picture of the progress towards a new way of working can be obtained.
This paper introduces a program successfully designed and implemented within ScottishPower, a major UK Utility. The program focuses on the development of an integrated Process Safety Management system. The program has since been developed, tailored and implemented in 3 European based Offshore E&P operations. The program is based upon adopting UK HSE guidance on developing process safety indicators (HSG 254) and supported by numerous other international standards including ISO55000:2014 (Asset Management) and API Recommended Practice (RP) 754, Process Safety Performance Indicators; the program has driven positive change through awareness and understanding of Process Safety and asset Integrity risk at every level of staff and contractor in the organization. Central to the program is the development of quantitative Key Performance Indicators from a Bow Tie Hazard identification and analysis process. This paper explains the methodology of how Bowtie theory can be used to develop leading KPIs covering an entire asset base; illustrated with a walk through of relevant Hazards, Bow Ties and KPIs. The paper will also articulate how ScottishPower utilized the KPI data within a near time KPI dashboard system, to enable staff at all levels to understand the current status of the risk control barriers across all assets regardless of age, type and level of automation. This innovative approach to KPI development and management enables ScottishPower to identify Safety, Human and asset risks in real time and to implement proactive solutions at facility and boardroom level before critical barriers fail.
Al-Jasmi, A. K. (Kuwait Oil Company) | Al-Zaabi, H. (Kuwait Oil Company) | Goel, H. K. (Kuwait Oil Company) | AL-Hamer, M. (Kuwait Oil Company) | Vellanki, R. (Halliburton) | Singh, S. (Halliburton) | Villamizar, M. (Halliburton) | Moricca, G. (Halliburton)
Each year, oil companies experience declining production rates and face challenges in terms of sustaining production targets, diagnosing well problems, and designing solutions to address such production decline. Identifying problems and opportunities at the correct moment, without losing time, is critical to the success of a digital oil field's (DOFs) intelligent solutions.
Traditional industry solutions involve using historical data for surveillance. In DOFs, tools are available to assist engineers with diagnosing fields made up of thousands of wells using instantaneous real-time data. With multiple reservoirs and thousands of wells in a field, it can be extremely challenging to diagnose, identify the opportunity and make right decisions collaboratively to optimize the well without losing time. This paper describes a multidimensional surveillance (MDS) approach using real-time and historical data, which can handle thousands of wells more effectively for problem identification and optimization. This solution is coupled with an action tracking system to assist the Engineers in monitoring the Field implementation and assess the opportunity collaboratively.
This paper presents the results of the application of intelligent agents to traditional work procedures to help increase production performance and final oil recovery in the Sabriyah KwIDF (SA KwIDF). SA KwIDF is part of a strategy undertaken by the operator to enhance asset performance using ground-breaking redefined DOF concepts. These concepts involve tightly integrated well instrumentation to provide enhanced data availability, power, and communication infrastructure to help improve field control, a new concept of collaborative centers to enhance asset team cross disciplinary integration across physically separated locations, and, finally, platform and production optimization workflows to increase effectiveness through automating work processes, helping shorten observation-to-action cycle time. The approach can make more effective problem identification and optimization possible. MDS acts as string facilitator when troubleshooting well performance and optimization. MDS allows engineers to track the implementation of suggested actions in the field. MDS approaches also allow engineers to compare wells side by side, to better understand the reservoir behavior, enhance the optimization process, asset awareness, team efficiency, and ultimately provide improvement to short-term production rates.
The MDS approach used in the Kuwait integrated DOF SA KwIDF involved 133 wells and the operator has projected to expand the system to an additional 500 wells in 2015. The primary objective of this initiative project was to maximize and sustain oil rates while controlling well decline and honouring safe well operating envelope constraints.
This paper describes the use of data mining agents to help enhance the optimization process, asset awareness, team efficiency, and, ultimately, provide improved short-term production rates.