Qatargas (Qatar)

Abstract We have shown previously that while total porosity is the weighted sum of density and neutron porosities, hydrocarbon volume is the weighted difference of the two. Thus, their ratio yields hydrocarbon, or equivalently, water saturation (Sw). In LWD environments where negligible invasion takes place while drilling, we investigate whether Sw derived from LWD density-neutron logs could approach true Sw in unknown or mixed water salinity environments. In such environments, it is well known that Sw determined from standalone resistivity or capture sigma logs is uncertain due to large water resistivity (Rw) or capture sigma (Σw) changes with salinity. On the other hand, the water density (ρw) and hydrogen index (HIw) variations with salinity are much less (Table 1). Hence, the water point on the density neutron crossplot does not move with salinity as much as the water point on a sigma-porosity crossplot does. Similarly, the water point on a resistivity-porosity Pickett plot would move drastically with changes in Rw. Also, because the hydrocarbon effect on density-neutron logs is much less in oil than in gas, the weights in the density-neutron porosities can be conveniently set at midpoint in light oil-bearing reservoirs without compromising porosity and saturation results. Thus, a quicklook estimate of Sw from density-neutron logs is the normalized ratio of the difference over the sum of density and neutron porosities. The normalization factor is a function of the hydrocarbon density. We also build a graphical Sw overlay for petrophysical insights. We tested the LWD density-neutron derived Sw in two Middle East carbonate oil wells that have mixed salinity. The two wells were extensively studied in the past. In the first well, the reference Sw is given by the joint-inversion of resistivity-sigma logs, corroborated with Sw estimated from multi-measurements time-lapsed analysis, and validated with water analysis on water samples taken by formation testers. In the second well, comprehensive wireline measurements targeting mixed salinity such as dielectric and 3D NMR were acquired to derive Sw, and complemented by formation tester sampling, core measurements, and LWD resistivity-sigma Sw. In both wells, density-neutron quicklook Sw agrees surprisingly well with Sw from other techniques. It may lack the accuracy and precision and the continuous salinity output but is sufficient to pinpoint both flooded zones and bypassed oil zones. Since density-neutron is part of triple-combo data that are first available in well data acquisition, it is recommended to go beyond porosity application and compute water saturation (Sw) in unknown or mixed salinity environments. The computation is straightforward and can be useful to complement other established techniques for quick evaluation in unknown or mixed water salinity environments.

Abstract This paper is focused on bringing insight into the risk imposed on asset life cycle management by obsolescence of spares and equipment, it elaborates upon various aspects related to obsolescence, causes, classifications and impact. The paper also demonstrates the comprehensive proactive Obsolescence Management Process developed and adopted by Qatargas, the world's premier LNG company, and highlights associated challenges, benefits and forward path. This will be a power point presentation with several pictures, diagrams, texts and graphs. The author, with his significant experience in asset lifecycle management will take the audience through definitions and classification of obsolescence with practical examples. The presentation then provides insight into the risk imposed by obsolescence on asset life cycle management and how availability and asset life cycle costs are impacted due to obsolescence. As the presentation progresses it will demonstrate a process that can be adopted by oil and gas operating companies to proactively mitigate the adverse effect of obsolescence with appropriate case study. Oil and gas sectors are very much asset based industries and they are highly dependent on technologies and automation for their efficiency and safety. With the fast moving technological environment around the world due to innovation, scientific research and commercial reasons; the products are getting obsolete at rapid rate. It is estimated that 10 to 15 per cent of items enter into obsolescence phase within the first five years of their installation, especially electronic items. There are even instances when items get obsolete before actually put into operation. This scenario has significant impact on reliability, availability and life cycle cost of the asset. This also imposes a great risk of emergency and unplanned upgrades and thus a great challenge in Physical Asset Life Cycle Management. Qatargas Obsolescence Management Process is a world class business process and is an important step in the physical assets' lifecycle management. This process helps engineers and operators to proactively identify, monitor and mitigate the adverse impact of obsolescence of spares and equipment on the asset availability and reliability. The process also supports the highest level of active asset life in the project design, thus ensuring maximum asset availability with a high level of spares and service dependability. Obsolescence is a new challenge and there is limited awareness about its impact. Obsolescence Management Process is not a common process in oil and gas operating organizations. This presentation will provide insight to the participants from operating organizations, what risk their own organization is having and how they can adopt this world class process. This presentation will highlight the challenges, preferences and expectations of operating organizations regarding Asset Life Cycle, Obsolescence and Spares/Service support requirement to the Manufacturer Companies, EPC companies, Consulting Organizations, Spare part Suppliers and software providers.

Abstract This paper describes Qatargas' new approach of linking the physical modeling and engineering workflows with a robust corporate data management backbone to improve the efficiency and effectiveness of an engineering team tasked with production planning, forecasting, allocation, optimization, well integrity, flow assurance, and reservoir management. To build a virtual integrated model of the field, component-level models such as the models for the reservoir, the wellbore, the production network and the injection network are developed and then dynamically linked to one another to ensure the consistency of the overall field-level model. The modelling tasks require a wide range of data acquisition and processing activities (such as production and injection monitoring, well test and PLT analysis, PVT sampling and analysis, corrosion monitoring and environmental monitoring for waste water, H2S and CO2). These data are normally captured using systems in the field. Traditionally, Qatargas has operated the wells and reservoirs based on manual workflows such as manual analysis of production testing and fluid sampling, manually estimating the performance envelopes, and then calculating the optimum feed-gas volume & composition, while respecting plant's limited handling capacity of H2S% and other non-hydrocarbon components in the feed-gas. The main challenges with this approach are the very significant quantity of man-hours invested in the routine activity, and the level of accuracy in estimating the right course of feed-gas volume & composition during irregular plant operation conditions such as shutdowns, slow-downs, machine trips, as well as supporting special studies or projects. In the new approach, the collected data are quality checked and loaded into the corporate database, as well as the various physical models. Once the physical models are established, then further analysis and "what if" scenarios can be carried out. The understanding of the field hence developed is propagated to the various discipline-specific models to ensure internal consistency between the models. In the course of the engineering workflows listed above, the corporate data store plays an important role in orchestrating the data flows in and out of the various physical models, and preservation of the results from disparate component models. It also enables the preservation of the previous versions of the models, by keeping an audit trail of data that were delivered to each instance of multiple models of the same kind (e.g., multiple reservoir simulation models, or multiple wellbore models). The adoption of a common data management backbone between various discipline-specific modelling activities promotes the development of a shared understanding of the hydrocarbon field amongst the team members that belong to different technical disciplines. Furthermore, web enabled visualization and data display tools decoupled from the complex modelling tools enable non-specialists to develop high-level insight into the individual modelled components of the field. The qualitative benefits of this new approach are clear to all involved; nevertheless, benchmarking data will be collected on the baseline workflows in order to quantify the overall benefits of the new approach.

Abstract In the beginning, even before the desert skyline was punctuated by stacks from the first LNG plants in the northeast corner of Qatar, air quality models were being run to estimate potential air quality impacts (AQIA) due to the proposed developments. AQIAs were required as part of the Environmental Impact Assessment (EIA) obligation to SCENR (now Ministry of Environment). With time, Ras Laffan (RLC) has grown into one of the premier energy hubs in the world with contemporaneous improvement in the pedigree of air modelling. This paper provides a genesis of air quality modelling in Qatar with initial screening estimates graduating to Industrial Source Complex (ISCST3) and now AERMOD as the mainstay model. The RLC coastline with its dense mass of industrial growth presents a daunting challenge in terms of predictions of air quality impacts due to a mix of uncertainties in emission inventories, coastal phenomena etc., which cannot be accounted for by the existing raft of air dispersion models. This paper makes a case for moving to an established scalable puff model (CALPUFF) operated in near real-time with dynamic Weather Research and Forecasting (WRF) data as the regulatory model of choice. The proposed platform will vastly improve RLC's Air Quality Management Plan (AQMP) by improving allowable increment planning, handle complex coastline conditions local to RLC, addressing the lacunae of plume models and photochemistry. Applications: Assess and mitigate air quality impacts from large gas and industrial projects through air dispersion modellingAQMP to assess future industrial growth using real-time AQIA Results and Conclusions: Results from preliminary one-year RLC WRF-CALPUFF compared against other platformsInnovative technological approach by leveraging dynamic computation platforms to improve air quality impact predictions Technical Contributions: Innovative air quality dispersion modelling using WRF-CALPUFF for assessing near real-time air quality impacts

Abstract Qatargas (QG) OPCO experienced an impressive growth in the last 6 years, starting with QG2 in 2006, and followed by QG 3&4, QG1 first in-fill drilling campaign, and finally QG1 second in-fill drilling campaign in 2012. These projects helped QG OPCO become the biggest LNG producer in the world. The sudden expansion did not allow the Well Engineering and Intervention (WEI) Division to easily assimilate the changes. Even though WEI grew in size (i.e. number of personnel) by recruiting global talent, this accelerated growth demanded a different approach to develop organizational capabilities (resources and processes) to operate the new well count. In 2012, WEI embarked on multiple initiatives to transition from a Project-oriented to an Operations-focused organization. The first initiative was related to the need for one overarching Well Integrity Management System since different criteria and procedures were utilized in each of the ventures. The second initiative addressed the need for a Well Control and Blowout Prevention Manual and Contingency Plan to prevent and mitigate well control situations in scenarios where Jack Up availability and Drilling organizations were limited (drilling organizations set up to expand QG OPCO were reallocated, downsized or dismantled). In addition, a new WEI structure was required in order to gain efficiencies, align personnel with business needs, and sustain or develop capabilities in the workforce. This paper discussed the journey of WEI in pursuing organizational capabilities in order to meet ever-demanding global energy challenges and QG OPCO vision - to be the world's premier LNG Company. Introduction The North Field, discovered in 1971, is the largest non-associated gas field in the world with estimated reserves exceeding 900 TCF. Qatargas is one of the four companies operating in the North Field. Since Qatargas establishment in 1984, the State of Qatar has become the biggest LNG producer in the world, providing reliable energy security globally and operating the biggest LNG shipping company internationally. Today, Qatargas is the largest LNG producing company in the world. Its role in producing North Field resources has a significant impact on the State of Qatar and the global LNG markets.

Abstract Qatargas Operating Company Limited (Qatargas) and Total Research Center Qatar (TRC-Q) established a collaboration to study Predictive Emissions Monitoring System (PEMS) algorithms for pilot application on a Qatargas turbine to predict NOx emissions. The approach for this Pilot Study was a blind benchmarking comparison of three main PEMS algorithms (first principle, statistical and neural networks) to define the most appropriate one(s) for application at the pilot gas turbine. This study was intended to demonstrate to local authorities that PEMS can be a reliable monitoring technique in both an alternative or complimentary capacity to CEMS. The assessment of PEMS models developed as part of this study and their corresponding performance will be discussed in a separate paper when the study is completed. This initial paper describes the challenges and lessons learned during the preparatory phase of PEMS model development. It describes the importance of a well-planned preparatory stage as this significantly affects the quality and validity of collected turbine operational and emissions data for PEMS model construction. It is important to undertake internal quality assurance checks on collected operational and monitored emissions data prior to model development. This paper describes the important role played by maintenance and calibration of measuring instruments such as stack emissions analyzers in ensuring reliability and accuracy of measured data. To build robust PEMS models, individual correlations between NOx emissions and various turbine operational parameters need to be assessed in the preparatory stage. The PEMS models developed for the pilot turbine were initiated on an incremental basis using variables with significant correlation and then optimized using other secondary parameters to improve correlation between the predicted and measured NOx emissions. This paper notes that the influence of turbine operational parameter on NOx emissions varies depending on its role in the formation of NOx as part of the combustion process. These include fuel gas composition and flow rate, as well as ambient air temperature and humidity.

Qatargas and Total Research Center Qatar (TRC-Q) established a collaboration to study Predictive Emissions Monitoring (PEMS) algorithms for pilot application on a Qatargas gas turbine to predict NOx emissions. This pilot study was intended to demonstrate to local authorities that PEMS could be a reliable technique in both an alternative and complimentary capacity to Continuous Emissions Monitoring Systems (CEMS). PEMS is an emerging algorithmic solution utilizing process and turbine operational data to estimate emissions from combustion units. Consequently, PEMS do not require routine maintenance and calibration, thus reduced costs, higher availability than CEMS. The approach adopted for the study was a blind-benchmarking comparison of three main PEMS algorithms (first principle, statistical and neural networks). The study was comprised of two phases. Phase 1 was PEMS model development and validation using turbine operational data and corresponding NOx data. Phase 2 involved PEMS model testing, where a different set of only operational data were used to predict NOx emissions. Predicted results of models were then assessed by comparing with corresponding NOx data from CEMS. The study continues, and this paper provides an overview of preliminary study results, challenges encountered and key lessons learned with regard to PEMS development. The initial study results indicated that there is no one specific PEMS algorithm that can be regarded as 'best-suited' to gas turbines that is able to cover a wide range of turbine operational conditions. Preliminary study results for the pilot turbine suggest that PEMS are best suited to predict NOx emissions within the operational range they have been trained for. Hence it is critical to have high quality and reliable turbine operational and monitored NOx data which covers the required range of likely operating conditions. Based on these initial results, the first principle and feed-forward neural network algorithms were found to perform better than the statistical algorithm. The full study results, incorporating improvements based on the above lessons learned, will be discussed in a future paper.