Malaysia's government recognizes the high risk that aging idle wells pose to its health, safety and environment and has developed some of the most stringent plug and abandonment, P&A, regulations to protect its future. Corroded casing strings and sustained casing pressure are common issues on its multi-decade old platforms and a risk-based design philosophy has been adopted to balance risk mitigation and operational costs, while still ensuring an eternal barrier. Both conventional rigs as well as rigless hydraulic workover units, HWU, are being used for P&A operations. This study considers the barrier element rationale applied in four offshore wells that were plug and abandoned by cap-rock restoration Perforate, Wash, Cement, PWC, barrier plugs. It also considers the operating window of a jet-based PWC technology to understand the challenges and opportunities for further optimization during HWU operations. Cap rock restoration utilizing both cup-based and jet-based PWC technology is being widely applied throughout Malaysia as a cost-effective alternative to casing section milled barrier plugs. Malaysia's P&A regulation allows isolation at the cap rock level, whereby "Contractor shall adhere to the Cap-Rock Abandonment Applicability Flowchart to identify technically and commercially acceptable candidates for this well abandonment method." The PWC method enables cap rock restoration in a single trip process; whereby the casing annulus is accessed by TCP guns to allow for annular debris to be effectively washed prior to cement plug placement. The process is not limited only to TCP guns; as a mechanical casing perforator was utilized in a shallow cased hole section of one of the wells to avoid damaging the outer casing. A custom BHA was developed and tested to match the cuts from the mechanical perforator. Specially oriented, rotating, wash jets were configured to maximize the annular access during the washing process. The washing effectiveness of this new BHA was confirmed by the massive amount of annular debris that was observed over the surface shakers. Operations were conducted offshore with a HWU with limited infrastructure and operating capability compared to a conventional rig. A PWC candidate screening matrix was applied early during the planning phase to manage rig limitation, well condition and operational risk to ensure successful barrier placement. All cap rock barriers were successfully installed and tested, and no sustained annular pressure remained in any of the wells. Fewer PWC plugs were required than originally planned, due to strict adherence to the Caprock Restoration Plan Decision Tree , resulting in significant cost savings for the project. The detailed time breakdown of the HWU operations provides useful insight into the operational efficiencies and unplanned events during the HWU campaign and lessons learned are shared from the project.

Adequate implementation of probabilistic dynamic modelling workflow allows to explore full range of solutions and helps in better decision making. Probabilistic modelling workflows comprise of a series of steps. This paper closely scrutinizes three key steps of a probabilistic workflow – sensitivity analysis, experimental design and model selection for decision making. There are different methods and processes practiced in the industry for these key steps. A comparative study of commonly available and practiced techniques is performed, and their pros and cons are demonstrated using open available Brugge field model. In the first part the commonly used tornado/one variable at a time (OVAT) is compared against other global sensitivity techniques suchas ANOVA (Analysis of Variance) and multi-regression analysis. The difference among the techniques is explained and its application is demonstrated through field example. It is shown that the global sensitivity analysis is more robust and provides better results but at a higher computational cost. Next experimental design techniques are reviewed with focus on the optimum experimental design. Key concepts of experimental design such as aliasing, resolution and confounding pattern are explained. It is shown that an appropriate experimental design can be used to avoid confounding of key parameters. The confounding patterns have been demonstrated for factorial design experiments. The concept is shown with the example of Brugge model. Model selection after history matching process is a challenging task. In the last part of the paper two different multi-dimensional visualization techniques PCA (Principal Component Analysis) and t-SNE (t-Stochastic Neighbour Embedding) are explained and utilized to visualize the ensemble of history matched models. These techniques in combination with k-mean clustering has been used to select representative models for prediction. The overall uncertainty captured by the representative models is compared to the full ensemble of history matched models. It is shown that clustering along with visualization provide a robust framework for model selection. The paper is intended to provide a good understanding of some of the key steps of the probabilistic modelling workflow. The technical concept behind each technique is briefly described but the main focus is on the practical aspects and implemtation of these techniques.

Subsea blowout preventer (SBOP) reliability is a major challenge in Deepwater Drilling & Completion operations, accounting for one of the major equipment failures and Non-Productive Time (NPT) costs yearly. This paper focuses on SBOP technological advancement since the Deepwater Horizon/Macondo incident in 2010, with additional emphasis on reliability, equipment condition monitoring and statistical root cause analysis. After finishing a deepwater well, the SBOP must undergo maintenance, repair if needed and pressure testing before being deployed on the next well. The rig owner is under great pressure to complete this turn-around to avoid waiting time. On an average, in-between wells, rig contractor took approximately 2.6 days extra time (NPT) waiting after completing top hole to get ready to deploy SBOP during 2019-20 exploration and appraisal campaigns. This can be critical during development campaigns where number of rig moves are involved quickly or in cases where top holes are batch drilled the waiting time for SBOP readiness can be as high as 7-8 days per well. Some operators are collaborating with drilling contractors in number of ways to arrange for a second fully assembled and (offline) pressure tested SBOP to be available on the rig (Dual SBOP); deployment of additional trained subsea engineers for performing maintenance/repair. SBOP pressure-testing time can also be drastically reduced by using comparative pressure-testing software to eliminate human error and accelerate pressure testing. Furthermore, leak detection time can be eliminated by installing sensors, and real-time test monitoring providing increased reliability with the additional advantages that conditional monitoring can be enhanced with the same digital sensors. SBOP dashboard that simplifies existing diagnosis and allow remote monitoring of the subsea SBOP control system will improve communication of SBOP health also serve common platform across rig fleets that allow standardization of SBOP diagnostic data and aids in operational decision making Ensuring additional SBOP redundancy especially while operating Emergency Disconnect System (EDS) available through Remotely Operated Vehicle (ROV) control panel or acoustic system. In addition, it is mandatory for the SBOP to have Autoshear and Deadman systems to be able to shut in the well in case of an emergency. Furthermore, technological workshop with several major service vendors have being held to ascertain current advances like Multifunctional profile, Accumulator recharged by ROV, ROV DP system, An Auxiliary Accumulator System and upgraded Acoustic System. In the end, the development of new technologies applied for the SBOP targets the overall cost optimization of the well lifecycle but also assure SBOP functionality. This paper is intended to provide considerations for operators in developing their future campaigns to frame scope of work for SBOP and rig contracting strategy.

Natural gas is the noble fuel of 21st century. Consumption increased nearly 30% in last decade. Exploitation of conventional, unconventional, and contaminated gas resources are in focus to meet the demand. There are number of giant gas fields discovered worldwide and some of them with higher degree of contaminants viz. CO 2 , H 2 S and Hg. Additionally, they have operating challenges of high pressure and temperature. It becomes more complex when discovery is in offshore environment. This study presents the development and production, separation, transportation and identification & evaluation of storage sites and sequestration and MMV plan of a giant carbonate gas field in offshore Malaysia. Geological, Geophysical and petrophysical data used to describe the reservoir architecture, property distribution and spatial variation in more than 1000m thick gas bearing formation. Laboratory studies carried out to generate the rock and fluid representative SCAL (G-W), EOS and Supercritical CO 2 -brine relative permeability, geomechanics and geochemical data for recovery and storage estimates in simulation model and evaluating the post storage scenario. These data are critical in hydrocarbon gas prediction and firming up the number of development wells and in the simulation of CO 2 storage depleted carbonate gas field. Important is to understand the mechanism in the target field for storage capacity, types of storage- structural and stratigraphic trapping, solubility trapping, residual trapping and mineral trapping. Study covers methodologies developed for minimization of hydrocarbon loss during contaminants separation and utilization of CO 2 in usable products. Uncertainty and risk analysis have been carried out to have range of solution for production prediction and CO 2 storage. Coupled Simulation studies predict the production plateau rate and 5 Tscf recovery separated contaminants profile and volume > one Tscf in order to have suitable geological structure for storage safely forever. Major uncertainties in the dynamic and coupled geomechanical-geochemical dynamic model has been captured and P90, P50, P10 forecast and storage rates and volumes have been calculated. Results includes advance methodologies of separation of hydrocarbon gas and CO 2 like membrane and cryogenics for bulk separation of CO 2 from raw gas and its transportation in liquid and supercritical form for storage. Study estimates components of sequestration mechanism, effect of heterogeneity on transport in porous media and height of stored CO 2 in depleted reservoir and migration of plume vertically and horizontally. Generation of chemical product using separated CO 2 for industrial use is highlighted.

Safety Critical Elements (SCEs) are the equipment and systems that provide the foundation of risk management associated with Major Accident Hazards (MAHs). A SCE is classified as an equipment, structure or system whose failure could cause or contribute to a major accident, or the purpose of which is to prevent or limit the effect of a major accident. Once the SCE has been ascertained, it is essential to describe its critical function in terms of a Performance Standard. Based on the Performance Standard, assurance tasks can be stated in the maintenance system to ensure that the required performance is confirmed. By analyzing the data in the maintenance system, confidence can be gained that all the SCEs required to manage Major Accidents and Environmental Hazards are functioning correctly. Alternatively, corrective actions can be taken to reinstate the integrity of the systems if shortcomings are identified. This paper shall detail out how the MAH and SCE Management process is initiated to follow the best industry practices in the identification and integrity management of major accident hazards as well as safety critical equipment. The tutorial shall describe in detail the following important stages: Identification of Major Accident Hazards Identification of Safety Critical Equipment, involved in managing Major Accident Hazards Define Performance Standards for these Safety Critical Equipment Execution of the Assurance processes that maintain or ensure the continued suitability of the SCE Equipment, and that these are meeting the Performance Standards Verification that all stages have been undertaken, any deviations being managed and thus that Major Accident Hazards are being controlled. Analyze and Improve Through the diligent application of these stages, it is possible to meet the requirements for MAH and SCE Management process giving a better understanding and control of risks in the industry.

The concept of minimum economic field size (MEFS) has been used by explorationists for almost four decades. MEFS is often the only filter to distinguish between a commercial and a non-commercial discovery—far before a wildcat well is drilled—to test a prospect for a working petroleum systems hypothesis. As simple as it gets, the concept started to lose traction in the 21 st century as subsurface targets became more and more challenging. In the case of tight hydrocarbons, it is fairly common to observe a P90 case net present value (NPV) to be negative, a P50 case to be positive, and a P10 case to be negative again. The reason for this outcome is that a whole set of full-cycle factors, in addition to the field size, affects prospect commerciality. Their uncertainty ranges can match or exceed resource estimate uncertainty. These factors include, but are not limited to, initial productivity of development wells, estimated eltimate recovery (EUR) per well, decline curve parameters, capital investments, operating costs, and the project phases’ durations. A new way of handling the full universe of risks and uncertainties faced by modern explorers is already available in the new generation of industry-leading integrated prospect risk, resource and value assessment software. Innovators and thought leaders can already substitute MEFS with a commerciality threshold (CT) that neatly mimics board considerations at the final investment decision (FID) stage gate. Others can consider the economic chance of success (ECOS) estimated with a probabilistic full-cycle mindset, as an additional metric valuable for risk management purposes. Using fictional case studies inspired by real-life assessment situations, we discuss the additional value creation by a CT-powered workflow as compared to an MEFS-based one and explain the reasons for the key differences. The discussed workflow does not eliminate nature-specific uncertainties; neither does it reduce the geological risk. However, it helps to better understand human-controlled risks and prepare management exploration decisions with a greater degree of confidence.

For any given Exploration oil and gas portfolio and associated opportunities, successful business decisions can only be made on the basis of technically robust estimates of the subsurface risk versus the resource potential and estimate of the associated upside(s). Ideally, these estimates should incorporate the entire spectrum of opportunities for the complete portfolio and they should be made in a consistent and comparable way. However, as Explorers, we are often faced with data that is incomplete, limited, of variable quality, and/or inconsistent. As a result, subsurface evaluation may be seen more as educated guessing rather than a robust science of evaluation grounded in facts and defensible logic. In their January 2020 paper "Randomness, serendipity, and luck in petroleum exploration" authors Milkov and Navidi (Ref 1 .) go quite a bit further and demonstrate that luck is a significant factor in the exploration success equation. They also showed a general lack in long-term consistency in exploration results of individual companies. Indeed, after we looked at PETRONAS’ own historical POSg versus actual technical success rates and observed only a fair to poor relation between actual technical success rate and the pre-drill POSg estimate. A similar—albeit less worrisome—observation was made for volume ranges and fluid phase predictions. Clearly, there is, and always has been, a phenomenal challenge for the Petroleum Geoscientists to provide the sought after estimates as accurately as possible, and we were no exception. In a bid to improve on this PETRONAS set out on a more disciplined approach to characterizing subsurface uncertainties on its conventional exploration efforts. Over time, more accurate characterizations of such exploration risk and resource potential have followed from a series of procedural guidelines, enhanced capability training program and careful governance of exploration workflows. With improved capabilities and workflow consistency, our evaluation teams have delivered substantially better subsurface evaluations. This in turn has led to more confident decision-making on e.g., individual drilling decisions and new play entries. While our newly implemented workflows are not groundbreaking in isolation, in combination they have delivered notable success and an improved ability to shape the future growth for PETRONAS. In this article, we will highlight the main contributing changes and demonstrate that the overall improvements are indeed impactful. We are not directly challenging the article of Milkov et. al., but are convinced that professionalism and scientific discipline is the deciding factor in the Exploration success equation .

This study intends to propose parameter adjustment for economic evaluation in considering abandonment expenditures (ABEX) during front end loading (FEL) of discovered petroleum resources. In maturing a petroleum field development, abandonment and decommissioning of wells and facilities requires consideration during FEL. FEL, long adopted by prominent E&P players worldwide is used to support capital investment decision-making where value i.e. expected monetary wealth is measured and evaluated by Discounted Cash Flow (DCF) analysis. However, the DCF approach is outdated and flawed where it does not capture fluctuation well. This study reviews ABEX of sub-commercial contingent resources or shelved projects and economic evaluation method used in the author's organization and their parameters, identifying relevant and applicable adjustments that could be made associated to ABEX. Combining the revised ABEX with a modified economic model, a proposed set of categorical adjustments to is produced. The results show that the ABEX of the previously sub-commercial projects yield a more competitive number with revised semi-detailed estimates and different escalation and inflation approaches. Additionally, a standardized assumption for abandonment year before cessation of production is recommended to provide a more realistic evaluation of when ABEX is actually required to incur. These, in turn, improves the Net Present Value (NPV) of the projects tested as well as their viability and rank towards being sanctioned for development.

The site selection of the facilities in oilfields is one of the important issues for surface engineers. In the progressive development of oilfields, new wells are explored and developed, and new process facilities (PFs) should be constructed to gather and process the fluid from these new wells. The emission of the PFs will affect the surrounding environment, including water sources, forests, and human settlements. Thus, the environment should be considered as one of the key aspects in the design process of facilities. Different locations of facilities in the oilfields will affect both the construction cost and environmental cost. Thus, a balance has to be found. In addition, the uncertainty of production rate of well fluid poses a great challenge to this problem. To solve the above problem, this paper provides a systematic methodology. The objective function consists two parts: construction costs and environmental cost. The solving algorithm has involved three layers of looping programming to calculate the value of objective function. First the weather conditions are generated by the Monte Carlo method, then the second loop is for the study areas, and the third loop is for the new facilities locations. After all the loops of iterations are completed, the objective functions are calculated, and the influence of the environment can be evaluated. Finally, the best solution can be obtained. The effectiveness of the proposed method is demonstrated through a design problem in an oilfield. The candidate locations for PFs are previously determined, and the optimal construction plan is solved by our method. The quantitative influence on the environment to these candidate locations can be evaluated. After determining the coefficient of the construction cost and the environmental cost, the best locations for the process facilities with the lowest total cost can be determined. A multi-objective model for the site selection of process facilities in oilfields is proposed, which has not been done by existing literatures. The construction cost and surrounding environment are both considered in the model. This work has the potential to serve as a decision-support tool for surface engineers in oilfields.

The objective of this paper is to share the holistic approach to managing aging fleet for offshore fixed steel structures. PETRONAS is currently operating a fleet of more than 200 fixed offshore structures in Malaysian water. More than half of it has exceeded the original design life. With enhanced oil recovery and other developing technologies, offshore platforms often than not are required to continue operating beyond its original design life. A holistic approach for life extension of fixed offshore structures are being developed to ensure safe operations of the facilities. PETRONAS has started SIM journey since 2007. The approach of Data, Evaluation, Strategy and Programme in line with API RP 2SIM set the basis for managing the integrity of the offshore fleet. An integrated solution was developed to manage both topsides and substructures. The Structural Integrity Compliance System (SICS) which houses the integrity management of topsides deteriorations to prioritize resources through risk based anomalies management. Risk based underwater inspection also formed part of the solutions, addressing mainly extreme storm in the region. Other Major Accidental Hazards (MAHs) risk ranking included in SICS are vessel collision and seismic. Management of facilities with minimal redundancy such as guyed wire monopod is addressed through time based inspection. A regional hazard curve is also developed to ensure the facilities are meeting the acceptance criteria set forth by the industry. Besides aging, other integrity triggers including shallow gas and subsidence required a different scheme in managing the integrity of the facilities, primarily addressed through a comprehensive monitoring programme. There is no one size fit all recipe in managing the aged platforms for life extension. The data plays a crucial role in ensuring the right methodology is deployed in support of digitalization and data driven decision making. Implementation of the system is proven to be reliable in ensuring the offshore fixed structures are intact to support safe and continuous operations to the operator in a cost optimum manner. The data analytics help to enhance the predictive model to optimize the inspection and maintenance programme. Strong ownership and commitment of the structural integrity engineers in ensuring the data integrity maintain the challenge in sustainability of the system and provide reliable source for data driven decision making to the operator. Category: Operational Excellence (136 - Managing Aging Facilities)

Production forecasts provide fundamental input to upstream business decisions, for example in resource volume reporting, field development and production planning. Very often in mature fields, having sufficient cumulative production, field forecasts are performed based on aggregating type curves for new wells and decline curve analysis for existing wells. Conventionally, they are derived by manual trend fitting which is subjective and any iteration incorporating new data is time consuming. The advanced analytics approach presented in this paper can provide rapid and credible forecasts along with more robust quantification of uncertainties as compared to the conventional manual approach. The first step involved in the forecast automation using advanced analytics is data integration in which the production, geological, and surveillance data are combined. Then integrated dashboards are created to infer trends in production behavior. Based on these trends at the most granular forecast level, petroleum engineering based algorithms are developed to automatically select the decline period. Thereafter, multiple scenarios of historical data are generated based on historical allocation and measurement uncertainty. Through each set of these historical scenarios, best fits curves following Arp's equation are extrapolated into the future to generate multiple forecast scenarios. Finally, field level forecast is generated by probabilistically aggregating individual granular level forecasts. The automated forecast program developed is computationally very fast as 500 forecast scenarios at the well level can be generated in 30 seconds, while the full field forecast with 50 wells takes about 30 minutes to generate. Automation of decline period selection and curve fitting eliminates the subjectivity by standardizing the process and reduces the chances of manual errors. Abandonment criteria, discounting and uptime variations can easily be accommodated in the automated process. Visualizations are utilized at each step for quality check and analysis. Forecasts from alternate methodologies are used to validate the forecast ranges coming out from this method. Uncertainties quantification in this approach is found to be more quantifiable and consistent compared to the conventional deterministic approach. Production dashboards created in this workflow by integrating production, surveillance, geological data and forecasts are a very effective tool to perform field reviews and communicate outcomes. The approach described above for decline curve analysis can easily be extended to any type-curve based forecasting. Automation of performance-based decline curve and type curve forecast methodologies has the potential to reduce huge manhours involved in their periodic updates by an order of magnitude that can be utilized to carry out other critical analysis. It is very useful in mature assets with large well inventory, huge dataset and where continuously new data are being added. Standardization of workflow, implementation ease and accuracy will tempt practitioners to use it and thereby develop skills in data analytics.

Decisions related to need of appraisal, extent of appraisal and sequencing of appraisal activities has always been tricky and complex. A well-defined process to reasonably compare the value likely to be created, not only by different set of appraisal options, but also by different set of possible sequencing is devised and illustrated through a field example using dynamically changing chance of development and corresponding expected NPV. Executing minimal appraisal to minimize upfront appraisal investment maximizes overall project revenue. Therefore it is critical to evaluate likely value creation by each appraisal activity and their dependency on each other. It is also critical to optimize sequencing to minimize appraisal investment and maximize value for information. The illustrated field's proved resources are not sufficient enough to support economically robust development; however field has potential to be commercially viable with some uncertainty that requires further appraisal. This field with complex structure, compartments, reservoir quality, areal extent and other contingencies is evaluated for various appraisal options using complex decision tree analysis. The example uses resource distribution targeted by each activity, likely development cost and crude price distributions as input to Monte Carlo simulator to arrive at expected NPV for all possible sequencing of activities. Chance of development for each appraisal activity, derived using multi-disciplinary approach, changes dynamically dependent on results of preceding appraisal activities in different branches of the decision tree. Expected NPV for each branch thus estimated, reasonably integrates all possible scenarios of cost, crude price and appraisal success and failures. This helps to perceive an appraisal strategy that best meets the field contingency scenarios, their chance of occurrence, project cost and price uncertainties, which allows taking informed and consistent decision. Complex situation, multiple options, dynamically changing chance of contingencies and their influence on successive appraisal is not easy to synthesize without systematic decision models. Such models, other than evaluating VoI of options, provide tool to evaluate value of flexibility and dynamically updated project value to help exit, end appraisal or farm out. Selection of appraisal activity and its sequencing is often guided by qualitative perception of uncertainty through mental short-cuts. Obvious appraisal sequencing based mainly on resource and chance may not always be the best option. Proposed method discriminates best option and optimal sequencing to help take consistent and quality decisions. This methodology attempts to include all possible scenarios and innovative multi-disciplinary contingencies for chance evaluation. This method attempts to evaluate meaningful expected NPV using Monte Carlo simulation with distribution of yearly average values of each influencing parameters (profile, cost and price) thus helping exploit ingrained opportunities in the uncertainty to generate value through repeated application. Method can be used to model complex appraisal scenarios to help chose optimal appraisal strategy. More complexities of possible scenarios can be added using the described example and methodology.

Manual Material Handling (MMH) activities (i.e. lifting, lowering, pushing, pulling, and carrying) pose the risk of injury to the musculoskeletal system of the workers. The injuries may lead to decreased work performance, and increased errors, absenteeism and health care cost. Therefore, it is necessary to know whether the workers of one of the Oil and Gas Company in Thailand are facing with the injury problem and to find the solutions for preventing those injuries. The procedure of the solution is walk-through survey of hazards and risks of Work-Related Musculoskeletal Disorders (WMSDs) among the workers (both onshore and offshore platforms) whose works involved in MMH tasks and providing the recommendation and followed up by Medical and Occupational Health team of the Company together with the physical therapists from one of the famous academic institute in Thailand who are collectively called "the research team". The research team conducted a walk-through survey and performed preliminary physical examination at an onshore and all offshore platforms. The survey included characteristics of each work, intensity and frequency of workload, work environment and potential risk factors. Physical fitness assessment including muscle power and weakness was also determined. The findings from the walk-through surveys, work procedure were analyzed to identify risk factors of work-related musculoskeletal injuries. The findings from preliminary physical examination in approximately 300 operating workers, upper cross and lower cross syndromes were found, indicating muscle imbalance in MMH workers. Task activities were analyzed on each procedure which has risk factors including awkward posture, repetitive movement, manual handling, forceful exertion, work environment, equipment, and cognitive and psychosocial issues. The research team reported risk factors with consequences and provided recommendation for each factor regarding engineering, administrative, and individual controls. For example, offshore platform has many floors which are designed for particular function (office, bedroom, dining room, etc.). Offshore workers regularly perform high repetitive stepping up and down stairs making them prone to hip and leg muscles imbalance. There is high compression at knee joint which may lead to early degenerative joint or knee pain. The recommendation is to provide properly work break and training with principle of ergonomics in workplaces including ergonomic risk factors; risk prevention; and strengthening exercise. Findings on this study lead to the specific designed exercise program and training to counteract the effect of heavy musculoskeletal activities related to MMH in daily work. The study will also serve as a guide for developing of MMH guideline for the company.

Currently, decisions involving a huge amount of money and resources are based on the outcome of simulations. Consequently, an increase in the computational resources has attracted engineers and scientists to increase the complexity of the mathematical models and their numerical algorithms to design or analyze various systems. On the other hand, the transparency of these models has decreased and the sensitivity of the outcome of these simulations on, for instant, certain assumptions or input parameters is often not known a priori. Furthermore, validation of the models for complex systems in which full scale measurements are limited or even impossible, gives more challenges to the decisions made based on these numerical simulations. Additionally, robust design to consider off-design conditions in a systematic manner is of great importance in oil and gas industry. Therefore, it would be essential to develop a systematic procedure to quantify the sensitivities and errors associated with each set of simulations in an objective manner. Uncertainty quantification (UQ) could be used as a tool to provide the validity of a performed simulation. This study describes the current research and opportunities for parametric analysis and uncertainty quantification in the flow assurance such as in performance prediction and validation, risk assessment, decision making and robust design. Additionally, we describe the importance and benefits of probabilistic analysis instead of deterministic analysis. Uncertainty quantification increases the computational expenses and effort of performing deterministic simulations. Therefore, an efficient method to quantify the uncertainties such as probabilistic collocation or surrogate models are required to reduce the computational costs without losing the accuracy of the analysis. Parametric analysis and uncertainty quantification will give more insights in the validity of simulation data than traditional methods. For instance, using the newly proposed methods will expose the sensitivity of the simulation results to off-design conditions and consequently leads to a systematic decision making based on the probability of an event occurrence. These methods could be used to quantify the most sensitive parameters in the system and the effect of their propagation to the outcome of the model. It is shown that performing non-deterministic analysis in some cases is a necessity.

There is an ever increasing need to extend the life of aging offshore structures beyond their original design life. Whether these structures are fixed offshore rigs or floating facilities, operators are continually looking for requalification and extension of the service life aimed at ensuring the integrity of the structure. With a continued requirement to produce oil or gas, either from the original fields or as a base for neighbouring subsea completions, many of these respective offshore installations are likely to remain operational for a period of time in the foreseeable future. The ageing offshore infrastructure presents a constant and growing challenge. Ageing is characterised by deterioration, change in operational conditions or accidental damages which, in the severe operational environment offshore, can be significant with serious consequences for installation integrity if not managed adequately and efficiently. In order to ensure technical and operational integrity of these ageing facilities, the fitness for service of these structures should be maintained. Maximising the availability and productivity of the field, whilst operating safely and with minimal impact on the environment, is a major concern for requalification and life extension. Structural integrity management (SIM) and inspection campaigns are important inputs in the Asset Integrity Management and the maintenance of structural integrity is a significant consideration in the safety management and life extension of offshore installations. Detailed integrity assessments are needed to demonstrate that there is sufficient technical, operational and organisational integrity to continue safe operation throughout an extended service life. Information on history, characteristic data, condition data and inspection results are required to assess the current state and to predict the future state of the facility and the possible extension of service life. However unique environmental conditions, type of structures, fabrications and installation methodologies used in Middle East required attention in particular, development of hazard curves and risk mitigation of the potential regional degradation mechanisms. During the life-cycle of an offshore structure the ultimate capacity is also an important attribute that affects the life expectancy, requalification and life extension of the facility, and can significantly influence the reliability levels and operational costs. This paper presents state of art practices in life extension of existing offshore structures and an overview of a regional hazard curves evaluation methodology and proposed regional correction factors represented risk to the integrity of a facility and the required procedures and re assessment criteria for deciding on life extension in particular the one with the importance for Middle East region. This paper also provides an overall view in the structural requirements, justifications and calibrations of the original design for the life extension to maintain the safety level by means of a maintenance and inspection programs balancing the ageing mechanisms and improving the reliability of assessment results

Uncertainty quantification on reserves estimation is a fundamental task for project validation on offshore green fields and also for the selection of a development plan that is robust to those uncertainties. To test the robustness of several development plans is then fundamental to be able to evaluate the impact of this uncertainty with low computational cost. Structural, geological and dynamic uncertainties are integrated in a new workflow based on a set of embedded response surfaces. The first step consists in building an experimental design with only structural and geological parameters to evaluate the impact of such uncertainties on static responses such as oil initially in place. In the second step, this experimental design is completed with additional dynamic parameters. The novelty of the static and dynamic design merging is to incorporate the previously obtained static response surface in addition to the uncertain parameters. By embedding the static response in the variables we obtain a response surface on the reserves more regular and more predictive. In this workflow, mainly applied to green fields, the impact of local geological uncertainty is usually of second order respect to other global uncertainties and it is taken into account as random noise whose variance may depend on the static response. The method is applied to a real green field with complex multi-level heterogeneities and it is compared to more standard approaches such as Monte Carlo. We manage to obtain a very good response surface quality for the static response (oil in place) that is then embedded in the reserves response surface. The final accuracy of this response surface is sensibly increased respect to a classical approach considering only static and/or dynamic parameters at minor costs in terms of reservoir simulations. The new approach reduces sensibly the number of reservoir simulations needed for an accurate uncertainty and risk assessment when applied to select the more robust development plan selected from a set of possible candidates. It also provides useful sensitivity analysis measures to guide possible data acquisition campaign in order to reduce risks and take better decisions.

Abstract Geohazard can be described as local and/ or regional site and soil conditions having a potential of developing into failure events causing loss of life or damage to health, environment or field installation. PETRONAS Carigali Sdn Bhd (PCSB) is confronted with various geohazards challenges, both in our domestic and international projects. These geohazards includes but are not limited to different types of soil stratigraphy, shallow gas, shallow channel systems, faults, pockmarks, mudflows, corals etc. In order to address these Geohazard issues, PCSB has developed a comprehensive Geohazard Assessment Model (GAM) for institutionalization within the company a governance process to manage geohazard risks. The aim of Geohazard Assessment Model is to: Provide a comprehensive integrated geohazards risk assessment framework to ensure geohazards risks are identified and management is given advance recommendations on the mitigation solutions and Ensure all requirements for the Geohazard Analysis such as technical; pre, during and post operations and health, safety and environmental (HSE) are within code compliance and assets are being safeguarded at all times. The importance of GAM can be reflected in terms of monetary, time, quality and HSE whereby substantial cost savings can be achieved via a risk assessment perspective. GAM utilizes an integrated approach requiring expertise in geotechnical, geophysical and geological disciplines to rationally identify, assess, quantify and mitigate these geohazards for the eventual selection of final field development concept, optimum asset sitting and approaches. This paper shall present how jack-ups suitability are studied from a risk assessment perspective using GAM. The importance of proactive measures to mitigate geohazards are also elaborated on projects undertaken by PCSB. Adopting a systematic approach to quantifying geohazard risk can minimise time and cost impacts upon any field development.

Abstract Production optimization of hydrocarbon fields is a complex task, due to the high number of processes involved and their synergy. It is therefore challenging to manage an integrated production system, comprising the reservoir and the production wells, the gathering system, and the process plant. The optimization process is driven by input data subjected to uncertainties due to variability, observational errors, and lack of up-to-date measurements. As a consequence, the aforementioned uncertainties are carried out through the simulation process up to final results. This work presents a workflow for oil & gas production optimization under uncertainties, that is structured in three phases. In the first phase, the integrated optimization process with powerful evolutionary algorithms takes place. The result is an optimized field configuration characterized by a deterministic production increase in respect to the initial production. After having identified the uncertain input variables in the optimization process, a Monte Carlo simulation for the optimized field configuration is performed. In this second phase, the input data are modelled through statistic distributions, and the obtained result is a probabilistic distribution of the production increase. The third step is a detailed analysis of the system constraints to evaluate the behaviour of the optimized configuration. In addition, a methodology to choose an alternative setting of the production field, that allows to increase the system reliability, is presented. The integrated workflow has been applied on a case study. The results obtained show that is extremely important to identify and quantify the uncertainties involved in a hydrocarbon production system for the evaluation of its potential production, and to verify the compliance with its constraints. Applying a configuration when a high degree of uncertainty is present may be hazardous and unsafe. The proposed workflow is an important tool to evaluate the field potential under uncertainties, optimize production, improve operations and system reliability, and support the decision-making process. Introduction Oil and gas production optimization is a field of extreme importance, since oil demand is continuously increasing, and many reservoirs currently in production are experiencing a decrease in production rates. The high number of processes involved and the synergy between them make the optimization of hydrocarbon fields a very complex task. It is therefore of primary importance to manage the challenges associated with an integrated production system, comprising the reservoir and the wells, the gathering system, and the process plant. In order to holistically optimise the production system the proposed integrated approach aims to combine subsurface models, surface production and engineering models in a single asset management tool.

Abstract One of the most useful features of decision analysis is its ability to distinguish between constructive and wasteful information gathering. Value-of-Information (VOI) analysis evaluates the benefits of collecting additional information before making a decision. VOI models describe the relationship between the uncertain quantities of interest, the reliability of the information and the decision criteria. Many uncertainty quantities are continuous in nature and the probability distributions that describe their uncertainty are discretized, and presented in decision trees, to simplify analysis. A three-point discretization of a continuous distribution is standard, and often preserves the main characteristics (central tendency, spread) of distributions that are close to symmetric. However, VOI studies rarely, if ever, include an analysis on the sensitivity of the VOI to the quality of the discretization. In this work we investigate a variety of discretization techniques, for a range of typical information gathering situations. The investigation utilizes a robust model that accurately calculates the VOI for any combination of continuous distributions. The key criterion for assessing a discretization techniques is whether or not they it has a significant impact on the decision to collect the information. The goal of the work is to provide practical guidance on the level and technique of discretization required. Introduction. Most of what petroleum engineers or geoscientists do involves "acquiring" information, with the aim of improving decision-making. "Information" is used here in a broad sense to cover such activities as acquiring of data, performing technical studies, hiring consultants, or performing diagnostic tests. In fact, other than to meet applicable regulatory requirements, the main reason for collecting any information, or doing any technical analysis, should be to make better decisions. The fundamental question for any information-gathering process is then whether the likely improvement in decision-making is worth the cost of obtaining the information. This is the question that the VOI technique is designed to answer. The oil and gas literature includes a number of papers (e.g. Begg et al, 2002; Bratvold et al, 2008) and books (e.g. Bratvold and Begg, 2010; Newendorp and Schuyler, 2003) where VOI analysis is introduced and described in detail. It is common to use decision trees to structure and evaluate the VOI decision. If the underlying uncertainty is continuous in nature, one of the common discretization methodologies such as Extended Swanson-Megill, Extended Pearson-Tukey, or the McNamee-Celona is often used to discretize the underlying uncertainty into a few, usually 2 or 3, degrees(Bickel et al. 2011). In general, the discrete probabilities and values are selected with the aim of matching the moments (mainly the mean and variance) of the continuous representation. In this paper we refer to this approach as the Low Resolution Decision Tree (LRDT) approach. A few papers discuss the calculation of VOI when the uncertain event of interest is continuous (Chavez & Henrion, 2004; Arild, Lohne, and Bratvold, 2008; Bickel, 2012). However, the VOI calculation approach in these papers is presented using different terminology than what is used in the LRDT approach. Furthermore, the papers are either limited to relatively simple problems or have very different representations of the reliability or quality of the information gathered. Despite the fact the LRDT approach can have significant errors, VOI calculation are rarely conducted using the full continuous representation of the uncertain event of interest. We suspect that the reason lies in the issue discussed above.

Abstract In 2010, a risk analysis taking into account both uncertainties in reservoir parameters and at the same time accounting for real time pore pressure measurements during a drilling operation on the Norwegian continental shelf (NCS), was completed. The plan was to drill through seven reservoirs, each independent of each other with a vast amount of uncertainties in the pore and fracturing pressure due to years of injection and production. The risk analysis was first made as an input to the drill plan, using best reservoir parameters available as well as expert judgments. Simulations were performed within a Monte Carlo (MC) framework. Results like for instance probabilities for fracturing and kick for drilling through the next reservoir(s) or tripping out, as well as an optimum mud weight for reducing risk, were obtained. The analysis was updated real time as the pore pressures were logged during the drilling phase, providing the best possible risk picture and giving the decision takers a possibility to update the drill plan before drilling through the next reservoirs. This paper describes how the main characteristics of the reservoirs were modeled and how the risk analyses were performed. Simulations obtained, both before and during the drilling operations, are presented. Results like the probabilities of fracturing and kicks as a function of reservoir parameters and number of reservoirs drilled, as well as optimum mud weight for risk reduction, are emphasized. Introduction Risk analyses are widely used by operators on the Norwegian continental shelf (NCS) to manage risk during drilling and completion phases. The analyses should reflect potential hazards and unwanted events during the operations, and are mainly based on pre-assumptions and best available information. Uncertainties of well specific parameters are usually dealt with in a conservative manner. However, risk analyses taking into account real time pore pressure measurements have, to the author's knowledge, not previously been performed. Considerable geologic uncertainty introducing potential narrow pressure margins were key issues in the planning of drilling a sidestep in a mature oil and gas field on the NCS. The field has been produced for some time, and several reservoirs had been subject to injection. Forthcoming decisions related to selection of mud properties and operational procedures were a challenge, and the overall risk level of the drilling operation was unclear. It was originally decided to undertake a quantitative risk analysis (QRA) to improve the overall risk picture and to form a stronger basis for decision-making in the development of the drilling program. Later in the project, one clearly saw the advantage of having the risk picture re-evaluated as the pore pressures were measured. The objective of this paper is to present the stages of the QRAs in the well planning process, the results and their applicability as decision support for the project. The QRAs was performed by use of the KickRisk analysis tool. This tool is based on a pressure control model, where factors influencing the bottom hole pressure (BHP) throughout the various phases of the construction of a well section are decomposed. The model includes down hole, fluid, equipment and operator-related aspects and their impact on hydrostatic, friction and surge/swab pressure effects. The analysis implies gathering of input on the uncertainty of these quantities in terms of probabilities and probability distributions. By Monte Carlo simulation, the resulting probabilities of entering a state of underbalance with the pore pressure (kick) and of exceeding of the fracturing pressure (fracturing) are derived, and the factors that are main contributors to the risk are identified.