This study aims to examine the application of pattern recognition technologies to improve the time and efforts required for completing a successful history matching project. The pattern recognition capabilities of artificial intelligence and data mining techniques are used to develop a Surrogate Reservoir Model (SRM) and use it to perform the assisted history matching process. A well-known standard reservoir model, PUNQ-S3, was selected to examine the potentials of SRM in an assisted history matching process.
SRM is a prototype of full field reservoir simulation model that runs in a matter of seconds. SRMs are built based on a spatiotemporal database. The database includes different types of data extracted from a few realizations of the simulation model. In this study, the SRM was developed using ten geological realizations of PUNQ-S3 reservoir simulation model. The uncertain properties are distributions of porosity, horizontal, and vertical permeability. The SRM requires low development cost and has high implementation pace. The SRM was coupled with the Differential Evolution (DE) optimization method to construct an automated history matching workflow. This workflow is able to produce multiple realizations of the reservoir, which match the past performance.
The developed SRM showed a high accuracy in mimicking the behavior of reservoir simulation model. Once we select the best performing cases during history matching, we were able to also obtain relaiable future forecasts for the model. The results of this study prove the cability of SRMs in assisting history matching process using population-based sampling algorithms and other computationally intensive operations in reservoir management workflows.
Deshpande, Alisha (University of Southern California) | Dong, Yining (University of Southern California) | Li, Gang (University of Southern California) | Zheng, Yingying (University of Southern California) | Qin, Si-Zhao (Joe) (Chevron U.S.A. Inc.) | Brenskelle, Lisa A.
A frequent problem experienced throughout industry is that of missing or poor quality data in data historians. While this can have many causes, the end result is that data required to perform analyses needed to improve facility operations may be unavailable. This generally occasions delays and wastes valuable time, as the data analyst must manually "clean up" the data before using it, or could even result in erroneous conclusions if the data is used as is without any corrections.
This work has uncovered a dynamic principal component analysis model-based method to detect the presence of erroneous data, identify which sensor is at fault, and reconstruct corrected values for that sensor, to be stored in the historian. However, the dynamic principal component analysis model-based method is not appropriate for all sensors, so a second method for detecting errors in data from a single sensor and calculating corrected values has also been developed. Both methods work on streaming data, and thus make corrections continuously in near real-time. The dynamic principal component analysis model-based method has been successfully tested in the field by injecting errors such as a missing, bias, spike, drift, frozen, etc? into real streaming operating data from a Chevron facility. The single sensor data cleansing methodology has not yet undergone field test, but has been tested offline using operating data into which errors such as drift, spike, frozen, missing… have been introduced. Use of these methods can ensure that good quality data for needed analyses is available in the data historian, thereby saving analyst time and assuring that erroneous conclusions are not reached by using faulty data.
Williams, Thomas (Baker Hughes) | Lee, Erik (Baker Hughes) | Chen, Jeff (Baker Hughes) | Wang, Xiaowei (Baker Hughes) | Lerohl, David (Baker Hughes) | Armstrong, Greg (Baker Hughes) | Hilts, Yero (Baker Hughes)
An operator had a well producing 100 percent water-cut and wanted to identify the origin of the water production using fiberoptic sensing technology. The vertical well was cased, perforated, and hydraulically fractured. A production string that included an electric submersible pump (ESP) at the bottom was run into the hole to a depth immediately above the top perforations. An additional 1,500 ft. of one-inch diameter rods were hung from the bottom of the ESP to convey fiber-optic cable into the perforated region. Fiber-optic cable was clamped onto the outside of the production tubing, ESP, and rods as they were being run in hole. Distributed temperature sensing (DTS) and distributed acoustic sensing (DAS) data were collected from the fiber and used to identify the water ingress locations using several techniques including DAS energy computation, DTS temperature trace evaluation, and DTS heat transfer models. The analytical methods were in agreement on the distribution of fluid entering the well. The effect of an operating ESP on DAS measurements was also investigated.
The deepwater operational environment worldwide poses a number of key challenges in the areas of real-time data accessibility, logistics, personnel skill assessment and training, and equipment maintenance, as well as heightened health, safety, and environment (HSE) requirements. A key to quality service delivery is to reduce or even eliminate costly incident and downtime by improving job planning and preparation, which heavily relies on the accuracy and timely availability of field data, and the ability to collaborate with onshore experts by the field crews.
A rig-centered digital platform was developed that offers the following key features: reliable network connectivity; real-time data sharing across the globe; job monitoring via video and remote human-machine interface (or HMI, the computerized application interface for executing field jobs and acquiring operational data) viewing; field crew and onshore expert collaboration by chat, file sharing, and voice-over-Internet protocol (VOIP); on-rig asset tracking and reporting; mobile application for job workflow management; seamless two-way data exchange between field and corporate data center; and business continuity via offline operations support for an intermittent connectivity environment. The solution applied the best industry practices and cutting-edge digital technologies to provide a reliable, flexible, and secure platform for the best possible team collaboration practices, together with key improvements in processes, tools, and job workflows from both the operations and the maintenance sides.
Pilot projects were launched at various offshore operations and insightful data were collected. Benefits such as improved decision making—anytime, anywhere—by enhanced collaboration, improved speed reacting to abnormal events, crew reduction, less exposure to HSE risks and downtime, and adherence to the best and most up-to-date service provider and industry practices, among other advancements, were achieved.
Exploration and production workflows continue to evolve in complexity. We aspire to transfer both data and interpretations across a wide range of domains, often using a variety of software applications throughout the reservoir management life cycle. Keeping the model updated with new information while characterizing the range of uncertainty is a continual challenge.
RESQML is the data exchange format used in the upstream oil and gas industry for transferring earth models between software applications in a vendor-neutral, open, and explicit format. RESQML V1.1 focused strictly on data exchange; only individual elements (such as interpreted picks, surfaces and reservoir grid) could be exchanged. In RESQML V2.0 (published in September 2014), capabilities have been expanded to provide a mechanism for transferring the relationship information between RESQML data-objects and to define more data-objects, for example, wells and unstructured simulation grids. With these new relationship capabilities, it is now possible to exchange structural & stratigraphic and reservoir frameworks, with their associated properties.
This paper describes how the RESQML format has evolved from a single exchange of independent geometry and property representations of horizons, faults, and corner-point structured grids, to a comprehensive consistent earth model exchange that can capture, represent and transfer many of the relationships and interpretations contained in a reservoir model.
Examples are drawn from the use cases and unit tests that demonstrate the use of RESQML to support partial model transfers, to describe structural and stratigraphic frameworks used in larger scale models (such as velocity models), and associate features and interpretations of those frameworks with specific representations, especially reservoir grids. The paper shows how these enhancements can be used to update and execute sensitivity workflows on a reservoir model, while describing uncertainty in the data used to build the reservoir framework.
An early form of the digital petroleum engineering career track has been in place at ExxonMobil's
BP began experimenting with unmanned aerial systems (UAS) in 2006 believing we could ‘just purchase one’ and fly it. The FAA had another view! Since then we have gained a great deal of knowledge about UAS: capabilities, appropriate applications for oil and gas (O&G), safe operating procedures and how to get UAS licensed and in the air. In the fall of 2013 BP, collaborated with the University of Alaska Fairbanks, the Federal Aviation Administration (FAA), and AeroVironment (AV), culminating in June 2014, with the first permission to fly a drone commercially over land in the United States. This purpose of this paper is to share our learnings and promote the use of UAS in the O&G industry.
Azaman, Dzulkarnain B (PETRONAS Carigali Sdn Bhd) | Shahari, Shahrizal B (PETRONAS Carigali Sdn Bhd) | Majinun-Helmi, Hendry (PETRONAS Carigali Sdn Bhd) | Sayung, Colinus Lajim (PETRONAS Carigali Sdn Bhd) | Dato’ Wan Hassan, Wan Mamat (PETRONAS Carigali Sdn Bhd) | Wong, Lee Hin (Schlumberger) | Salim, Muzahidin Muhamed (Schlumberger) | M Som, Mohamad Kasim (Schlumberger) | Biniwale, Shripad (Schlumberger)
Samarang is an oil field located offshore Sabah, Malaysia. The field was developed in 1975 and comprises wellhead, production and gas compression platforms, as well as personnel living quarters. The majority of the platforms have fully pneumatic instrumentation / control and shutdown systems, with no provision for remote monitoring/control. The field is undergoing a major redevelopment project that consists of two phases:
As part of the redevelopment plan, Samarang was the first field selected for an end-to-end asset management Integrated Operations (IO) project. Its implementation deliver objective and focus at the whole of the asset operation optimization through increased level of monitoring and surveillance, diagnosis, optimization and operations transformation in a way that fits PETRONAS Carigali Sdn Bhd (PCSB) current IO state as well as future expansion plans.
The IO program aims to help Samarang to address growth, organization and environmental challenges by integrating new transformational technologies with streamlined work processes, enabling:
an effective working environment
streamlined and automated work processes
the availability & accessibility of quality information across the organization
collaboration of expertise across multiple locations, teams and domains
intelligent alarms and alerts for continuous asset awareness
an increased in hydrocarbon production and recovery.
Since commissioning of the project, we have reported the lesson learned and early benefits in various SPE conferences. A summary of reported benefits are as follows
An innovative parallel design process was invented to improve the efficiency and integrity of the design process
An IO model was described and defined for framing, planning, design and execution of the project
Heightened monitoring and surveillance through installation and retrofitting of measurements and control devices, telemetry and telecommunication infrastructure, HMI (human-machine-interface) and surveillance system
Introduction of “Functional Base with Asset Mindset” collaborative working environment across multiple locations, team and domains
Total asset optimization hybrid of steady-state and transient-state flow simulation technologies
Quality and efficiency improvement in terms of decision making cycle from months to a daily
Work efficiency improvement by more than 80% of staff time saving
Consistent value creation with production impact more than 10,000 bopd since Phase I commissioning
Being the first field selected for IO implementation means that best practices, lessons learned; processes and procedures, frameworks and operating philosophy garnered during this IO implementation will serve as invaluable references for IO implementation in other assets. This paper describes the reusability and repeatability of the Samarang IO process and its procedures, the inventorization of deliverables and how these could be leveraged and scaled up for application to other assets (or a larger scale implementation).
Wang, N. (State Key Laboratory of Offshore Oil Exploration) | Cheng, Z. (State Key Laboratory of Offshore Oil Exploration) | Lu, Y. (Department of Engineering Mechanics in Tsinghua University) | He, B. (CNOOC International Limited) | Ren, G. (Department of Engineering Mechanics in Tsinghua University)
Drilling automation can replace manual operation with more predictable and consistent computer-controlled drilling process, as well as enhance the digital link between well-site and data center and enable the remote drilling. In this paper, the automation of drilling process is viewed as three hierarchical control loops: the trajectory control loop, the drilling state control loop, and the motor and actuator control loop.
The drilling state control loop, which controls the bit attitude, weight on bit (WOB), etc., is pivotal to the trajectory control, and remains a focus of development. The drilling state control loop is composed of three sub-loops, i.e., the surface loop, the downhole loop and the hybrid loop. The surface loop supervises the torque, pressure, and hook load, controls the WOB, and takes actions in emergencies. This loop is being analyzed in auto driller systems, and in managed pressure drilling. The downhole loop controls the directional drilling tools, and is well realized in rotary steerable systems. The hybrid loop, which connects the prior two loops and reinforces one loop with the other, however, is not well developed.
A slide drilling system is presented as an example, where no downhole loop holds the bit attitude, and the hybrid loop faces sharpest challenge to control the drilling direction from surface. A joint control of top drive and drawworks is developed to perform coupled control of WOB and toolface. The control system takes into account the desired toolface and WOB. Different operating modes of slide drilling systems are identified and applied with specific control algorithm. The shift between modes is controlled by the operator.
The control system is developed and simulated with the aid of multi-body dynamics modelling of the drill string. This drill string simulation model, based on absolute nodal coordinate formulation, receives the control commands for the top drive and drawworks, and outputs the sensor data and the well trajectory. Loads and dynamic response of the drill string are also simulated. Multi-body dynamics method enables real-time full-hole drill string simulation, and is a powerful tool for automated drilling system development.
Ekkawong, Peerapong (PTT Exploration and Production Plc.) | Kritsadativud, Pannayod (PTT Exploration and Production Plc.) | Lerlertpakdee, Pongsathorn (PTT Exploration and Production Plc.) | Amornprabharwat, Anan (PTT Exploration and Production Plc.)
Gas fields in the Gulf of Thailand (GOT) share some similar operational complexities and experience many common challenges. Such challenges include the huge number of wells and platforms, and the large, complex, interconnected pipeline network. Additionally, each well, of course, exhibits different performance, different enhanced recovery as well as different and diverse flow assurance methods. Fluid streams also vary significantly from well to well; for instance, the differences in condensate to gas ratios (CGR), water to gas ratios (WGR), and the CO2, and H2S levels. Moreover, production performance in the GOT remains very dynamic. The decline in production could be seen early, even though proper reservoir management was achieved because most of the reservoirs were small and compartmentalized. Optimizations aiming to maximize revenue from these fields are very challenging.
State-of-the-art industry solutions to these problems are provided by integrated production modeling, and reservoir simulation. At first consideration, they appear to be reasonable tools that can physically describe the flow of fluid, whether in a reservoir, well or surface facility; however, these tools may not serve well for the complicated compartmentalized characteristics of the gas fields in the Gulf of Thailand. Currently, determining optimum natural gas production rates in the GOT is performed by manually fine-tune the production rate using information from the latest well testing data. This method may simple and convenient but requires large effort and does not guarantee the optimal solution.
This study presents a more efficient production optimization scheme integrating constrained optimization with decline curve analysis to predict future well production performance. The project net present value is translated into the objective function, comprising maximizing condensate production and minimizing waste water production while also honoring daily gas production nomination. Well performance, export specification, and the capacity of pipeline networks are formulated as system constraints. A linear programing optimization algorithm is then used to solve the resulting optimization problem for a single time step. Next, the optimization is integrated with the production decline trend from the decline curve analysis to obtain the forecast of future production performance.
Tested against the production data of a large gas field in the Gulf of Thailand, this method showed a significant increase in the condensate production and a decrease in the water production. This solution not only enhanced production, but also reduced tedious time required for modeling, history matching, or manually configuring well production. Main assumptions, limitations and the conclusion of the proposed method are also included in this study.