March 2015 Keynote Presentation
Innovators -Technology Enthusiasts
•Learn about emerging technologies
•Strong aptitude for technical information
•Like to beta test new products
•Willing to ignore the missing elements
•Love to demonstrate their own expertise
•Expect unrestricted access to top technical people
•Have little to no budget to spend
Early Adopters -The Visionaries
•Gain dramatic competitive advantage via revolutionary breakthrough
•Great imaginations for strategic applications
•Attracted by high-risk, high-reward propositions
•Will commit to supply the missing elements
•Perceive order-of-magnitude gains —so not price-sensitive
•Want rapid time-to-market
•Demand high degree of customization and support
Early Majority -Pragmatists
•Secure productivity improvements via evolutionary change
•Reliable managers of mission-critical applications
•Astute about real-world issues and tradeoffs
•Strong preference for proven applications
•Prefer to go with the market leader
•Insist on good references from trusted colleagues
•Want to see the solution in production at the reference site
Late Majority -Conservatives
•Avoid falling too far behind market norms
•Not comfortable with new technology
•Highly reliant on a single, trusted advisor
•Need simplified standardized pre-assembled solutions
•Need value-added services but do not want to pay for them
•Maintain the status quo.
•Good at debunking marketing hype
•Disbelieve productivity-improvement arguments
•Believe in the law of unintended consequences
•Seek to block purchases of new technology
•Not a customer
•Can be formidable opposition to early adoption
Crossing the Chasm - Two Key Principles
•Target a “beachhead” segment
•Highly focused approach to “rekindling the flame”
•Niche market with an intractable problem, not solvable by conventional means
•Process owner is under pressure to find a solution
•Pragmatists are willing to consider disruptive approach
•Commit to provide the “whole product”
•Bring all the ingredients with you
•Complete solution to the intractable problem
•Typically involves products and services from partners and allies
•Lead vendor takes responsibility for ensuring customer success
Azaman, Dzulkarnain (PETRONAS Carigali Sdn Bhd) | Majinum-Helmi, Hendry (PETRONAS Carigali Sdn Bhd) | Shahari, Shahrizal (PETRONAS Carigali Sdn Bhd) | Lajim Sayung, Colinus (PETRONAS Carigali Sdn Bhd) | Dato’ Wan, Hassan Wan Mamat (PETRONAS Carigali Sdn Bhd) | Hin Wong, Lee (SCHLUMBERGER) | Muhamed Salim, Muzahidin (SCHLUMBERGER) | M Som, M Kasim (SCHLUMBERGER) | Biniwale, Shripad (SCHLUMBERGER)
The Samarang field is located offshore Sabah, with operation office in Kota Kinabalu (KK), Sabah and Headquarters in Kuala Lumpur (KL). The Collaborative Working Environment (CWE) is one of the key components in Samarang Integrated Operations (IO) project, it is desired to collaborate actionable data and information across the expertise from multiple disciplines and geographically split locations in order to have faster and better decision making processes.
A Tri-Node CWE was designed and implemented in Samarang IO project. It integrates new transformational technologies with integration of offshore data streaming and work processes and has enabled the followings:
collaborative expertise of multiple domain and locations across geographically split of Samarang offshore, Sabah operations and headquarters
enables a more effective working environment
work processes are streamlined and automated
quality information is available and accessible across the organization
management by Exception
increase hydrocarbon production and recovery
The Tri-Node CWE is adopting immersive model whereby Samarang Asset Team was co-located and work in CWE. The model is selected considering lesson learned from industry projects that if employees have to schedule the use of a separate collaboration room (Distributed Model) and leave their workspace to go there, they are less likely to do so.
The Tri-Node CWE is housing Samarang asset team with shared visualization of data, KPIs, workflow execution with surveillance by exception, collaborative decision making with action tracking and management. The aim is to closely coordinate synergistically the decision making processes across different domains and functions in efficient manner.
The implementation of IO for Samarang field represents a major change in the way that Samarang asset will manage the operation of Samarang in both the daily and long term operation of the field. As such it qualifies as a major technology project and change management is a vital and ongoing part of the alignment, planning and implementation of the project.
The change management is recognized as one of important components in Samarang IO Framework to ensure stakeholders are in alignment as well as its sustainability. J.P Kotter and Prosci Adkar change management models are being used to execute the change management Plan.
The goal of this paper is to describe the Tri-Node CWE and change management implementation in Samarang IO project and underscores its challenges and lesson learnt in integrating data from different technologies into work processes and enables multiple petro-technical domain expertise for decision making in collaboration manner.
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.
The complexities involved in the available reservoir simulation model for the geologic CO2 sequestration study at SACROC Unit, lead to a high computational cost nearly impractical for different types of reservoir studies. In this study, as an alternative to the full-field reservoir simulation model, we develop and examine the application of a new technology (Surrogate Reservoir Model – SRM) for fast track modeling of pressure and phase saturation distributions in the injection and post-injection time periods.
The SRM is developed based on a few realizations of full-field reservoir simulation model, and it is able to generate the outputs in a very short time with reasonable accuracy. The SRM is developed using the pattern recognition capabilities of Artificial Intelligence and Data Mining (AI&DM) techniques. The SRM is trained based on the provided examples of the system and then verified using additional samples.
The intricacy of simulating multiphase flow, having large number of time steps required to study injection and post-injection periods of CO2 sequestration, highly heterogeneous reservoir, and a large number of wells have led to a highly complicated reservoir simulation model for SACROC Unit. A single realization of this model takes hours to run. An in-depth understanding of CO2 sequestration process requires multiple realizations of the reservoir model. Consequently, using a conventional numerical simulator makes the computational cost of the analysis too high to be practical.
On the other hand, the developed SRM for this case study runs in a matter of seconds. The comparison between the results of SRM and simulator, during training and verification steps of SRM development, demonstrates the ability of SRM in mimicking the behavior of numerical simulation model. The results of this study are intended to prove the potential of AI&DM based reservoir models, like SRM, to ease the obstacles involved in the conventional CO2 sequestration modeling.
Over the past decade, Total has deployed several Digital Oil Field (DOF) pilot projects across a large number of assets. Building upon successful elements of earlier deployments, each subsequent project has provided valuable feedback, leading progressively to a better recognition of Total's business needs as well as to the identification of key functionalities.
The new generation of DOF platform deployed on one of the Total operated assets in Deepwater Nigeria is capitalizing on all the earlier experiences. The Nigeria application is focusing on improved production and reservoir management through implementation of a shared asset model (data management), real-time monitoring dashboards (data visualization), automated well-test validation workflow (business process), and capability to track well events (events management).
Further to the evaluation of actual benefits gained from the Nigeria deployment, this paper reviews the journey of Total's DOF implementation, evaluates its current status, and discusses its future direction. The paper shows some concrete examples of gain in production and time saving brought about using the collaborative platform provided by the DOF installation.
The paper shares the challenges faced by Total during DOF implementation, the step-wise approach it took to overcome these challenges and, finally, the key elements which led to the successful deployment in Deepwater Nigeria. It also shares insight on workflows foreseen to be implemented in the near future.
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.
This paper discusses principles of well barriers and how they have been applied to a large, diverse well inventory across multiple countries and fields. Well barrier principles are discussed with practical definitions and examples applied to typical well types. Then the work processes and lessons learned during implementation of these principles to a large collection of wells are reviewed. Implementation of well barrier principles has provided a common approach to viewing wells and has improved consistency in management of well risks.
Implementation of well barrier principles, and application to a large, diverse well population is early in industry adoption. The fundamental concepts of well barriers have been present in the literature since the publication of NORSOK D-010, version 3, in 2003. However, outside Norway uptake of these principles has been limited. As the industry recognizes the simplicity and broad applicability of the well barrier approach, work processes are being modified to align with these principles. Industry standards relating to well integrity, such as ISO 16530-2 (Well integrity for the operational phase), are using well barrier principles as a basis for design of specifications. This paper describes lessons learned while implementing this significant work process change.
In the past few decades, Coal-Bed Methane (CBM) has become an important source of energy especially in North America. The methane adsorbed within the coal is in a near-liquid state. The open fractures in the cleats are commonly saturated with water. In developing the CBM reservoir, water in the fracture spaces and coal seam must be continuously pumped off from coal seam. This reduces pressure and desorbs gas from matrix.
Although operators desire to produce hydrocarbon quickly, a too fast dewatering rate can irreversibly damage matrix desorption process, which can lead to an unfavorable ultimate recovery. Further, the aggressive production rate can potentially release the coal fines and drive them into pump, which increases maintenance effort and cost. Even worse, the de-watering process fluctuates because of the rock porosity and permeability changes resulting from the brittle coal seam and pressure reduction. Therefore, it is critical to adjust the pump operating parameters in a timely manner to maintain a continuous/intermittent production.
The 10 CBM wells at study locate in a mountain with difficult access. Previously engineers had to evaluate well performance and optimize the pump on-site, which is limited by a monthly basis. We firstly developed an automatic data processing system using the advanced Echosounders, which can measure the water level in real time. The reservoir pressure can be then monitored dynamically through interpreting the detected water level. With an automatic-wireless data transferring system installed on-site and a closed-loop control program to receive, process, and interpret data, the pump operating parameters can be changed in real time through remote control. This system not only identifies the downhole problems in real time, but also reduces the pump maintenance frequency from 40 days to 75 days statistically and numbers of trip to well site. Further, the gas production rate has been averagely improved by 30% for the 10 wells.
The authors firstly developed an automation data processing and control system in the favor of advanced echosounders. Based on the interpreted reservoir pressure, we can avoid aggressive production by adjusting the pump operating parameters in real time, which eventually results in a better ultimate recovery. The developed workflow (automatic echosounder data acquisition, real filed data transferred to central office, data processing, interpretation, and simulation in computational system, adjustment commands to operating system) is especially valuable for the locations difficult to access.
Drilling technology for oil and gas exploration has evolved continuously based on feedback from operational experience. With the operators' focus on drilling more challenging unconventional wells, the biggest drivers are efficiency and operating cost. Operators want more real-time information during drilling to provide early warning of potential problems and take corrective actions. Service providers are meeting these challenges with technology improvements that are more robust and automated. The main focus for service providers is improving reliability throughout the service life of the tool while reducing maintenance cost. Consequently, in the past few years there is growing interest in the oil and gas industry towards developing technology and tools to gather more real-time downhole data and use analytical algorithms for fault diagnostics and health prognostics of components in drilling systems. This paper develops the framework and algorithms for constructing data-driven component life models and utilizing them to optimize operational efficiency and extend the life of the drilling system. The key driver behind this approach it to minimize the overall life cycle cost of tools which includes the cost of maintenance and cost of failure. The optimization variables are maintenance intervals and operational parameters (e.g. rpm, weight on bit, etc.) that should be tuned to achieve a desired level of drilling efficiency and reliability. Mathematical models for predicting the life of critical components in the drilling system is developed a-priori by using design qualification test data, operational data, drilling dynamics and historical FRACAS (Failure reporting analysis and corrective action system) information. The framework developed in this paper utilizes these predictive life models for making operations and maintenance decisions at various stages during the life cycle of the tools. The methodology developed in this paper is used to optimize the operational parameters and maintenance intervals for two designs of the bottomhole assembly namely (a) rotary steerable system without motor and (b) rotary steerable system with motor. Tradeoff of maintenance cost and operational performance is studied for different level of operational parameters. The results presented in this paper show that significant improvements in operational efficiency and maintenance intervals can be optimized by using downhole operations and predictive analytics.
Big Data Analytics has steadily gained momentum in the upstream E&P industry. Much of the attention has been on advancing data-driven methods including empirical statistical and stochastic approaches, and especially artificial neural networks (ANN). The focus has been on the particular analytics method used rather than to the management, governance, and refinement of the data used in models. Studies conducted through the SPE and by global E&P companies have validated that data management is a major problem in the oil & gas industry. They have clearly established that over half the engineer's and geoscientist's time is spent just looking for data and assembling it before multidisciplinary analysis is even begun (