bluuebunny/crossref_metadata_2025 huggingface dataset:
Challenges to Backfilling an Existing Natural Gas Liquefaction Facility with Different Gas Specifications
Authors: Omar Abdelaziz Ahmed , Masi Silvia , Shehata Ahmed , Cannatelli Domenico , Cannell Christopher
Published: November 2019

The design of new natural gas liquefaction facilities is closely aligned with the quality of the immediately available feed gas and the SPA’s agreed with customers. However, the lifetime of the facilities often extends beyond the lifetime of both the gas source and the duration of the SPA’s. Recent statistics indicate up to 60 MTPA of global liquefaction capacity is not utilized.

Qualitative based approaches are often adopted to assess how an LNG plant responds to a change in feed gas specification. However a more valuable approach uses a quantitative analysis which can achieve an optimal outcome via individual tuning of a potentially large number of plant variables. Such an approach starts by performing actual plant capacity tests for different operating modes and process variables to capture baseline operation performance data. The plant test results are to validate a detailed plant simulation model which includes all the plant variables of interest. The validated model can then help identify the optimum operating condition and the benefits of a range of potential modifications.

The methodology was used to identify solutions to a typical problem in a multi-train facility where a change from rich feed stock was accompanied by the presence of aromatics in a significantly leaner feed gas. Detailed modelling of the plant enabled an understanding of the solubility of the aromatics in the lean gas. The previously validated model of the real plant behaviour was then used to evaluate the benefits of changes to the key operating parameters and minor modifications to the plant itself. This resulted in a significantly more efficient and cost-effective solution than simply importing LPG which would have been the solution normally taken by a traditional "qualitative" approach.

A similar approach was used to address an associated commercial challenge of satisfying a SPA demanding a high HHV with a leaner feed gas. In this case the solution relied not only on the technical insight afforded by the quantitative analysis but also a recognition that accurate tuning of the operational process allows a reduction in the conservatism of the product specification. Furthermore, with minor modifications, a multi-train process with segregated storage can be operated in multiple HHV mode provided careful procedures are employed to mitigate operational risks.

This paper demonstrates how a holistic, detailed, quantitative analysis of gas liquefaction process can provide a good insight into the capability of existing plant to respond to changes in feedstock quality. The outlined methodology combined with a good understanding of the commercial features of the LNG business offers the possibility to better exploit the significant and growing amount of unused gas liquefaction capacity around the world.
AI Based Real-Time Decision Making
Authors: Bek-Pedersen Erik , Lind Morten , Asheim Bjarne André
Published: November 2019

A real-time decision support solution for control room operators that targets increasing production efficiency by reducing plant upsets and disturbances has been developed. The solution relies on process engineering and plant knowledge combined with AI tools like ontology and rule-based reasoning. The aim is to enhance the basis for rapid and intelligent decision-making and increase human performance when abnormal situations occur.

The solution is based on "Multilevel Flow Modeling" (MFM), which is a modeling language that builds on a systematic representation of relations between objectives and functions of plant equipment in a means-ends structure. The aim of MFM in this context is to develop models that allows for reasoning about causes and consequences of events, or upsets in process plants, such as oil and gas production facilities. A key feature of MFM is its compatibility with human cognition and results of MFM reasoning can therefore be communicated to operators in an intuitive way.

In their daily life, control room operators are presented with process variables through a SCADA system and alarms to notify them that something is not right. Their task is to maintain the production in in the plant and avoid unnecessary disturbances and shut-downs, for which they often rely on the information they receive through the systems as well as their experience. When abnormal events occur, operators often need to respond fast, in order to avoid plant upsets, however, there are no solutions to support them with situation analysis and root cause identification. The MFM based solution samples plant data and develops "failure trees" in real-time that are presented to panel operators in a simplistic and logical way. The solution has been developed in an industry-academia collaboration, in close collaboration with two operators. Initial online testing has been done on pilot plant basis, whereas the piloting on offshore fields is currently ongoing. The modeling work is done in close collaboration with engineers and operators familiar with the targeted plant facilities, in order to secure that all relevant operational aspects are adequately covered. In parallel, a UX development path is ongoing and is part of the integrated test program.

The novelty of the decision support solution as well as knowledge about how it impacts control room operators in their daily work is discussed. The learnings and experience from offshore pilot testing are also presented.
Modeling Rate of Penetration using Artificial Intelligent System and Multiple Regression Analysis
Authors: Hadi Farqad , Altaie Hassan , AlKamil Ethar
Published: November 2019

Over the years, the prediction of penetration rate (ROP) has played a key rule for drilling engineers due it is effect on the optimization of various parameters that related to substantial cost saving. Many researchers have continually worked to optimize penetration rate. A major issue with most published studies is that there is no simple model currently available to guarantee the ROP prediction.

The main objective of this study is to further improve ROP prediction using two predictive methods, multiple regression analysis (MRA) and artificial neural networks (ANNs). A field case in SE Iraq was conducted to predict the ROP from a large number of parameters. A databases from one well drilled in carbonate environments were subjected to the predictive methods. Each raw dataset is described by eight parameters including rate of penetration (ROP), true vertical depth (TVD), weight on bit (WOB), bit rotational speed (RPM), torque (TQ), flow rate (Q), equivalent circulating density (ECD), standpipe pressure (SPP), and unconfined compressive strength (UCS). First, both MRA and ANNs are tested as predictive methods. The prediction capacity of each model was also verified by using two-based error metrics: the determination coefficient (R2) and the mean square error (MSE).

The current results support the evidence that MRA and ANNs are able to be effectively utilize the drilling data, and thus provide accurate ROP prediction. However, more attention to the multiple regression analysis is required where it is implemented for ROP prediction. ANNs appear to be more conservative in predicting ROP than MRA as indicated by a higher value R2 (0.96) and lower value MSE (1.89) of the ANN model. Considering the input parameters, the obtained results showed that TVD, WOB, RPM, SPP, and ECD had the greatest effect on estimated ROP-conditions, followed in decreasing by pump flow rate, drilling torque, and rock strength. Another important point that highlights in this study is that the drilling rate may increase with depth in carbonate rocks because of their heterogeneity. This study presents new models to estimate ROP from other parameters which can help the driller to achieve an optimal drilling rate through monitoring controllable parameters.
Using Analytics to Assess Health Status of DLE Combustion Gas Turbines
Authors: Parrella Ilaria , Bardi Francesco , Salerno Giuseppe , Gronchi Daniele , Cannavò Marco , Sparacino Emiliano
Published: November 2019

The trend towards digitalization is becoming stronger and disruptive. Thanks to the experience gained over the past two decades, rotating equipment OEMs are now able to connect assets from any location, transfer data with cyber secure protocols, run analytics on the fly and manage remotely alerts. The above helps to provide valuable insights to customers, partner with them and drive high availability and reliability, optimize operations and support maintenance decisions (Allegorico, 2014).

This paper addresses the problem of assessing the health status of a Dry Low Emissions (DLE) combustion system, one of the most critical components of a Gas Turbine. It describes how the combined use of remotely-acquired operational data and different types of analytics, which represents a digital replica of the system, is used in conjunction with expert's domain knowledge to drive planned and unplanned maintenance decisions.

We applied this strategy to an Oil&Gas plant and the results of the integrated service delivered have been observed for several months, providing feedback on the methodology as well as points of reflection for further enhancements.

The methodology presented here can be summarized in three phases: "Anamnesis"a set of data-driven models and analysis of historical events to characterize the DLE combustion system of remotely-connected units"Diagnosis"a cognitive investigation of the DLE mapping to detect instability symptoms, instrumentation failures, performance issues and emissions level and to identify the root cause of the combustion issues (hardware or software)"Therapy"Combustion issues resolution and relevant recommendations to support maintenance activities on-site

Furthermore, some real cases of combustion problems on the connected fleet and their respective solutions will also be presented.
Gas Reconciliation with Advance Error Reduction
Authors: Nidhi Varun , Rao Rakesh , Chhapolia Prakash
Published: November 2019

Pipelines are the most economically viable mode of transportation for oil and gas. Every pipeline is monitored 24×7 using meters distributed across the pipeline. Flow, temperature and pressure meters are the most common and essential for continuous and efficient operation of pipelines. Like any other instrument these meters also have uncertainty and prone to error due to irregular calibration, drift, gross error and other such events. The overall accuracy of pipeline metering increases as the distance between consecutive meters decreases. It is also affected by the placement of meters at critical locations like pipeline tapouts, tapins and consumers points. Economics do not allow pipeline operators to install beyond a certain amount of metering assets.

The complexity to efficiently calculate the product in and out of a gas pipeline is more compared to a liquid pipeline. It arises due to the high compressibility of gases compared to liquids. Gas pipelines operate at much higher pressure than oil pipelines. The trapped gas inside a gas pipeline can be called line pack of that pipeline. The line pack is very sensitive to two natural factors pressure and temperature of the pipeline. Oil pipelines carry one fluid at a time. Gas pipelines on the other hand carry several gases as a mixture. Unlike oil, gas billings are calculated as the energy the gas mixture carries to the consumer. Due to the mixture, gas composition is another essential factor to accurately calculate energy of the mixture.

This paper discusses the challenges of calculating various transport factors and phenomena in gas pipelines and how methods like gross error correction and machine learning can be used to increase the accuracy. The results and conclusions are derived from the applications of these methods to natural gas transportation pipeline. Some of most important conclusions obtained were Understanding the pattern of on-field meter data with ideal meter provides insights in the root cause of the problem. e.g. sudden spike in temperature leading to error in line pack.Creating digital twin of all metering assets allows faster isolation of pipeline sections having calculation errors. e.g. by monitoring the difference between field and ideal parameters.Having a central meter diagnostics system that combines the data from meters of different make and models improve the pattern recognition and error detection ability.Gross error detection isolates the meters inducing error. The feedback can be provided to the machine learning algorithms for root cause analysis.

Note: This paper only covers the gross error of meters. There are methods used to reduce other meter errors namely random, limiting and systematic not covered in this paper. Readers are requested to read relevant material to understand the complete scope of errors in metering systems.
Novel Realtime Tracer Technology for Continuous Well and Reservoir Monitoring
Authors: Nikjoo Ehsan
Published: November 2019

Inflow tracer technology is a proven, risk-free and cost-effective method for continuous well and reservoir monitoring. To enable steady-state inflow assessment in real time, RESMAN has recently developed a new generation of Realtime Tracer technology. After a few years extensive research and development, this patented technology has been recently piloted successfully. This paper describes the different aspects of this new technology for the first time and present the results of the pilot.

The Realtime Tracer technology consists of intelligent chemical tracers released by autonomous and wireless downhole injection tools and an in-line optical device at surface, which is retrievable and insertable during operation, for real time measurement of tracer signal.

Upon injection, the intelligent tracers partition into the fluid phases, e.g. water tracer goes to water phase and oil tracer to oil phase, and subsequently the tracer molecules are transported to surface according to the velocity of fluids. At surface an automated measurement method with high sampling frequency, up to 0.1 second, ensures capturing all the tracer features with high resolution. The measured tracer signal is simultaneously processed in real time, by applying a dedicated computational algorithm, so that the results of test can be ready shortly after finishing the tests.

The Realtime Tracer pilot was conducted in an onshore, vertical and water test well in Norway. The downhole injection tools were placed in two locations along the well while the in-line probe was installed on the surface line. Based on the results from the pilot, all aspects of this technology have been successfully validated and verified. This includes the performance of injection tools, detection of different tracers, measurement method and device, the dedicated software and its incorporated algorithm as well as inflow assessment to allow for relative production estimation.

The Realtime Tracer technology provides significant improvement in tracer technology as it enhances different aspects of the existing tracer technologies through testing a well during production (no need for well shut-in and thus no production loss), less human interaction by real time measurement with high frequency (no need for manual sampling), quick delivery of results etc. The latter, for example, will improve decision-making process significantly, enabling production engineers to optimise the well performance and helps them to mitigate the problems as early as possible.
Real-Time Production Optimization - Applying a Digital Twin Model to Optimize the Entire Upstream Value Chain
Authors: Okhuijsen Bob , Wade Kevin
Published: November 2019

The oil and gas industry is increasingly looking toward data-driven solutions to boost performance, enhance efficiency and reduce costs. However, understanding such complex systems requires the consideration of data from multiple sources in real time. Digital Twin modelling is at the heart of the next generation of real-time production monitoring and optimization systems. Using the integration of data, simulation, and visualization of the entire operating company value chain, from the subsurface equipment to central processing facilities, it's a solution that maximizes production. Such a system for Real-time Production Optimization (RTPO) has been developed jointly by Siemens and Process Systems Enterprise by combining their XHQ and gPROMS Oilfield technology.
An Integrated Optimization Model for CO2 Rich, High Temperature and High Pressure, Boundary Water Gas Field Group
Authors: Guo Lingling , Yang Yong , Ma Liangqian
Published: November 2019

As the main gas producing blocks of South China Sea, Y field group has four gas fields on operation and two gas fields on going. In production process, there is water influx, sand, carbon dioxide rich, high pressure, high temperature, limitations of the decarbonization ability, and hydrocarbon components content requirement of downstream users. This paper focuses on establishing the integrated model of reservoir, well, gathering network and provides forecasting schemas for meeting contract requirements.

This paper addressed the Y-gas filed group challenges and methodology of flow behavior from reservoir to pipeline, using the Petroleum Experts' Integrated Production Model suite of software (IPM). The integrated model lays special stress on high temperature, multi-layer IPR, liquid loading, CO2 component change. Model calibrations include the characteristics of water vaporization condensation, getting multi-layer IPR though iterating MBAL model and multi-phase flow model, analyzing rules between cumulative gas production rate and component CO2, N2.

Based on the objective gas field group integrated model, the production forecasting and gas supply optimization are studied to solve the problem of multi-system and multi-constraints while conventional research are difficult to achieve the global objective optimization. According to forecasting results, the contract requirements of 4.251 billion cubic meters can be met by 2025. Due to production decline, the gas field group has been unable to meet the contract since 2026. The terminal decarbonization capacity can fully meet the needs. 13-2 gas field is a high proportion hydrocarbon gas reservoir, and hydrocarbon gas volume of the gas field group could meet the contract demand during the whole production forecast period. Some preliminary results from this optimization are also presented.

In the future, the integrated model of simulation and optimization can be carried out in combination with other possible conditions. The objectives and constraints can be adjusted according to the change of production conditions, that optimize the development plans and submit the optimization results.
Dynamic Wellbore Modeling for Multilayered Gas Condensate Wells with Water Vapor Content Using Olga: A Case Study in Camisea Field.
Authors: Dorival Vargas Jose Miguel , Zuloaga Molero Pavel , Segama Candiotti Elizabeth , Gomez Erik
Published: November 2019

This paper discusses the historical evolution and future performance of wellbore dynamics in a multilayered gas condensate well (100 MMSCFD) in the Camisea Field - Peru. The analysis included the modeling of water vapor content produced within the gas condensate stream and its multiphase behavior and interaction with other fluids inside the borehole through the reservoir depletion under commingled production. The goal of this study is to quantify the impact of the wellbore dynamics in well deliverability and the effective recovery of hydrocarbons for each individual layer, and propose actions to achieve an optimum production scheme.

The approach is based on the modeling and matching of dynamic behavior of the wellbore and individual layers with the observed data during 9 years. For this purpose a dynamic wellbore model was built using OLGA. The inputs to the model are: 1) the properties for each productive layer characterized using multirate test combined with PLT logs to get the individual IPR's; 2) the model of the reservoir fluid to properly represent the retrograde condensation and the behavior of water in the vapor phase; and 3) the liquid levels in the wellbore from historical PLTs and density logs.

In the field case studied, the analysis showed that the water vapor present in the fluid stream is a fundamental key to understand the evolution of fluid levels inside the wellbore. This is quite important since the water in the gas phase is not usually included in the EOS for reservoir simulation purposes and its impact in the wellbore dynamics is neglected because of the very low BSW (less than 1%). For the well that has been studied, the analysis revealed that the lower zone was prone to stop producing due to the higher productivity of the upper reservoirs. Consequently, the increase in liquid level was a result of the production decrease and not vice versa. After the lower layer stopped producing, it was observed that there was a quicker increase of the liquid column between the upper and lower reservoirs. This column was mostly condensate but it was gradually replaced by water in the liquid phase, which came from the vapor phase produced in the upper reservoirs. This slow replacement represents a more restrictive condition for the lower reservoir, as the column becomes denser.

This study allowed for the understanding the complex interaction between retrograde condensation and water vapor behavior with the wellbore dynamics. The study also describes the process of the liquid accumulation during the decline of production of multilayered reservoirs which was successfully matched with observed data. As a result of the analysis, a new completion scheme was proposed to effectively recover the hydrocarbons in the layers affected by liquid loading issues.
Automated & Enriched Reservoir Inflow Performance Relationship Expedites Massive Wells Diagnosis to Assist Production Engineering Decision Making, Maximize Efficiency and Overall Asset Performance
Authors: Davila Rayner , Caicedo Sergio , Rubio Erismar
Published: November 2019

Enriched Reservoir Inflow Performance Relationship (ER-IPR) is a novel, expert and intuitive surveillance tool to manage by exception underperforming wells, using smart analytics for well problem classification, prescribe possible remedial actions or opportunity generation as well as predicting wells that potentially will cease to flow, exploiting smart field architecture capabilities by adding value to big data through meaningful visualization plots.

Although existing intelligent systems (IS) updates massive amount of well models automatically, engineers require high expertise and time to understand well performance, identify root cause problem and capitalize opportunities. The ER-IPR is a holistic approach that display multiple well operating-points in one normalized reservoir performance curve that systematically drives users to identify underperforming wells, along with their associated problems and give insights of the reservoir management strategy by implementing simple four steps: 1) Obtain all well operating-points from IS. 2) Generate the ER-IPR. 3) Execute expert diagnostic module. 4) Perform short-term production forecast for the highlighted problematic wells using Integrated Asset Model.

The tool was tested in a Digital Oil Field (DOF) in two different reservoirs. One reservoir is an anticline with a strong compositional gradient starting from gas condensate to black oil with saturation pressure and GOR varying in a wide range. The other one is an undersaturated reservoir under peripheral water injection with very stable bubble pressure and GOR across all areas. ER-IPR facilitates reservoir/sector/well performance review, providing proper well diagnostic evaluation as well as recommending possible remedial action based on analytics that consider the physics phenomenal of the production system and user performance acceptance limits according to field real time data observation. ER-IPR tool automatically compares, classifies and visualizes all wells simultaneously, translating big data in effective recommendations to sustain/increase production. ER-IPR minimize the number of inactive strings by proactively identifying wells that potentially will cease to flow, prioritizing well remedial actions, considering production forecast analysis and resources. ER-IPR enables proactive surveillance and monitors well performance thought nodal analysis over time, to have fully well behavior assessment and instantaneous asset awareness.
An Efficient Method to Control Oilfield Wells Through Advanced Field Management Ensemble Based Optimization Capabilities
Authors: Abbas Hicham , Mustapha Hussein
Published: November 2019

Field development optimization is a crucial and important problem considering economic and physical constraints. Advanced technologies such as smart wells and completions, and well performance optimization, allow for maximizing recovery without the need of additional drilling activities and all associated costs. This type of strategy applies to mature and challenging heterogeneous reservoirs with water and gas coning, and to low permeability and gas condensate deposits, and others. The presented method elegantly considers all field constraints and delivers predictive optimal well control settings taking into consideration reservoir uncertainties and offers optimized development strategies while minimizing on risk and cost.

We will present a new field development technology to optimize well performance in both oil and gas fields. The methodology allows for simultaneous optimization under uncertainty of multiple wells using surface constraints and subsurface flow control devices. The method is very fast and uses a combination of gradient and stochastic methods. Starting from an ensemble of realizations of the reservoir model, the best well control settings are searched using a steepest ascent gradient search method, where the search direction can be approximated using the cross covariance of the ensemble of objectives predicted using cloned reservoir simulations for a user defined future time horizon. The workflow solution employs several levels of computational complexity reduction combining advanced hardware architecture and localization algorithms to speedup optimization procedures and cover wider range of spatial uncertainty.

We present two examples that include control of surface liquid rates of producers and injection rates of injectors as well as controlling the flow control valves of producer and injector wells to demonstrate both efficiency and robustness of the optimization method. The method presented is very robust and offers with the highest confidence optimal field developments making best use of the most advanced software and hardware technology. The novel presented process results in significant cost reduction on drilling and other operational activities.
Development and Application of IoT Platform for Gas Lift Recovery
Authors: Yu Lei , Wei Wu Zhenghua , Zhigang Fang , zhensong Wang
Published: November 2019

Gas Lift Recovery IoT Platform is integrated IoT management and control platform self-dependently developed by Tuha Gas Lift Technical Center. Via real-time monitoring parameters e.g. surface tubing, casing pressures and gas injection volume etc., the behavior of gas lift valves can be reversely inferred by the platform. Through the control of the gas injection rate, the diagnosis of malfunctioned well and system optimization can be realized.

As to software, based on calculation method, basic logic algorithms for failure diagnosis & behavior diagnosis model are established. Further, by taking the profit maximization as target, mathematical model for gas lift system optimization is established. By taking mathematical model as core, early warning diagnosis & system optimization modules are developed. Further, the database, interactive expert and control & communication modules are developed. For hardware, based on flow characteristics, a new method based on flow measurement for variable opening degrees of regulating valve is proposed and flow control method of regulating valve based on incremental PID control is proposed.

As to software development, via study, the real-time monitoring and optimization diagnosis software for gas lift recovery IoT platform is formed, capable of realizing real-time diagnosis of gas lift well behavior and real-time optimization of the system. For hardware development, the integrated device for gas flow detection and control with STM32 as the core is developed. Through flow & opening control experiments, it is verified that the average measurement accuracy is up to 96%, the average control accuracy is 97.1% and the precision control of remote gas injection is realized. In combination with the control & communication modules for real-time monitoring, optimization and diagnosis software, the closed-loop control for gas lift recovery IoT platform integrating field data acquisition, remote diagnosis & optimization and field feedback execution is formed.

Gas lift IoT platform features safety, efficiency and good economics, capable of realizing real-time monitoring of production parameters as well as remote diagnosis, troubleshooting and production optimization etc., which significantly saves cost and could make up for the insufficiency of resources at fields.
Synchrotron-Based X-ray Micro-Computed Tomography for Real Time Investigation of Alkaline Surfactant Flooding
Authors: Aborshaid Hussain , Alzahid Yara , Mostaghimi Peyman , McClure James , Chen Cheng , Sun Chenhao , Armstrong Ryan , Asali Muhannad
Published: November 2019

Alkaline Surfactant (AS) flooding is an enhanced oil recovery (EOR) method to mobilize residual oil. Deatailed understanding of transport during these recovery mechanisms requires detailed pore-scale studies. This point leads to the utilization of X-ray imaging for its application in pore-scale characterization. Synchrotron-based X-ray imaging is an advanced technique that is capable of capturing the dynamics of pore fluids at the microscopic scale. The aim of this project is to investigate the pore-scale flow of AS flooding at two different salinities in carbonate rocks using real time 3D images collected by synchrotron-based X-ray imaging. The morphologies of the non-wetting phase are first computed, and oil recovery in the two scenarios is estimated. In addition, the wetting states of the two conditions are assessed by contact angle measurements. It was observed that optimum, or Winsor type III mobilized more oil, since it yielded a higher recovery value, as compared to under-optimum or Winsor type II-. Alkaline surfactant at optimal salinity was marked as an ideal condition that effectively reduces interfacial tension (IFT) to mobilize residual oil. This study provides insights in the pore-scale flow mechanisms that occur during AS flooding, which are important for understanding the basic EOR mechanism of this particular flood.
FEED Competition for Mega Upstream Projects
Authors: Al Ali Salah , Al Habshi Shaikh , Al Ashqar Ameed , Bin Ruken Saud
Published: November 2019

Executive Summary

In accordance with the Global trend in Oil & Gas industry to invest in innovation and smart growth and capital efficiency (Project Light), Al Yasat has launched a new contracting scheme for the accelerating the development of its marginal oil fields. In view of difficult oil recovery cost and ease of contractor capacity due to nearing completion of previous field developments such as SARB and Umm al-Lulu, Al Yasat has contracted three EPC firms to compete on Front End Engineering and Design of the Belbazam field development with a grand prize for the winning design being selected to immediately proceed to Engineering, Procurement and Construction. The two losing bidders will be awarded consolation prizes in the form of partial recovery of their investments on the FEED. The attraction of this contracting scheme derives not from cost savings during the FEED stage, but most importantly from the omission of the bidding process for the EPC stage, with the winning contractor bearing full responsibility of their design whilst being fully deployed, with little to no variation costs.

This paper discusses some of the success factors and concerns associated with design competition from a Clients perspective, with the objective of extracting the difficult oil from marginal fields in a profitable context.
Effective Supply Flow Control: An Oil And Gas Capital Project Case Study
Authors: Arbulu Roberto , Shenoy Ramachandra
Published: November 2019

In an earlier report, Professors Iris Tommelein and Glenn Ballard of the University of California, Berkeley, analyzed the underlying principles of the construction practice of Advanced Work Packaging (AWP) and provided a critique to its standard design, making a number of predictions. Fundamental in their critique is the operations policies and parameters that dictate the final behavior of a project production system when AWP is applied. They predicted undesirable consequences to the performance of project production systems including, but not limited to, inventory growth, longer cycle times, lower labor utilization and higher costs.

Using an Operations Science framework, this paper reviews a capital project example that implemented AWP and uses this project experience to validate each of the predictions previously made by Professors Tommelein and Ballard. This paper also introduces the technical approach that the owner operator adopted to address the project performance gaps created by AWP, which included the application of CONWIP control protocols for more effective control of supply of materials and permanent equipment to the point of installation.
MEIDP – India's Transnational Pipeline from the Middle East
Authors: Nash Ian , Spradbery Chas
Published: November 2019

This paper presents details of the development of the Middle East to India Deepwater Pipeline (MEIDP) providing information on the technical and commercial feasibility of the deepwater gas transportation system, which will reach a record water depth of 3450m, cross two continental slopes, an earthquake subduction zone (the Owen Fracture Zone) and outfall debris of the river Indus fan in 2500m water depth.

High pressure trunk lines have proved to be the safest, cheapest way of transporting gas to market for short to medium distances up to 2,500 kilometers, making the proposed SAGE - Middle East to India Deepwater Pipeline the optimal solution for gas delivery to the Indian Subcontinent. Linking Middle East gas fields of Saudi Arabia, UAE and Oman to India across the Arabian Sea for an offshore distance of 1200 kilometers. The MEIDP gas transmission pipeline is designed to transport up to 1.1BCFD gas into the Indian energy markets.

The economic and political drivers for such a project are presented together with details of the overall project cost and tariff calculation to allow successful gas utilization by India's gas starved and stranded power stations. The pipeline project history and current design status will be reviewed together with findings of the Marine Reconnaissance survey between Oman and India. The challenges faced by the project from both a design and installation perspective are discussed together with some of the detailed geohazard assessments performed for the pipeline crossing and active fault zone (OFZ) and the Indus Fan. The qualification plan developed with DNVGL is described together with details of the future construction schedule for first Gas.

As a project that builds from the Oman-India project of the 1990's; the changes in risk profile in terms of industry and vessel readiness are reviewed, and the readiness of the next generation of installation vessels to install such a pipeline is discussed.
Augmented and Virtual Reality Applications for Separation Solutions Improve Service Provider-Operator Collaboration and Technology Placement
Authors: Stephen Mark , Clarke Stephen , Kapila Ketan
Published: November 2019

Originally developed as an enhanced entertainment technology, advancements in augmented and virtual hardware catapulted the development and adoption of these tools for industrial applications and, more recently, the oilfield. These tools have vast use potential—from training to solution creation to operations. The benefits of newly developed applications for rigsite virtual mapping and holographic model viewing and the improvements they introduce for service provider/operator collaboration are discussed.

Conventional methods for rig survey execution and optimization for separation solutions, which include solids control and waste management technologies, can be time consuming, technically challenging, and costly. This can be particularly true for offshore operations where weather, logistics, drilling programs, and environmental regulations can rapidly increase project complexity. Additionally, the equipment selection process commonly involves communication and collaboration with multiple subject matter experts (SMEs) and operator representatives globally in an iterative and labor-intensive process that lends itself to the opportunity for miscommunication and errors.

The preliminary use of rigsite virtual mapping and holographic model viewer applications is to demonstrate improvements to cross-organizational collaboration. Using rigsite virtual mapping allows specialists to conduct a rapid wide area scan (RWAS) to accurately map the rig space to create fully functional and scaled three-dimensional (3D) models that can be used in augmented reality (AR) and virtual reality (VR) to develop more robust visualization of system design and placement. Using rigsite virtual mapping allows the separations solutions team to more safely, efficiently, and accurately measure and scan a rig space to achieve the optimal equipment orientation and integration. The improved visual assets enable better alignment with operator teams.

Using the holographic model viewer allows 3D models of where separation equipment can be placed within the physical space at the rigsite or in the office. On location, rig surveys can be enhanced by enabling personnel to more effectively identify obstacles that might impede installation. For the office, the service provider and operators can more effectively discuss technologies and technology solutions using enhanced visualization. Bringing the technology "into the room" enables more dynamic solution creation and ideation.

A renowned global research and consulting firm lists AR/VR as a major emerging technology trend that has the capability to influence how companies do business. The application of these technologies can revitalize remote engineering collaboration and personnel optimization and reduce service/product delivery costs. Already successfully introduced in other markets, AR/VR in the oilfield has the potential to expand rapidly and become an essential tool for more efficient operations.
Highly Scalable Digitalization Platform for Oil and Gas Operations Enables Total Asset Visibility for Predictive, Condition-Based Fleet Management Across Single and Multiple Sites
Authors: Settemsdal Stig Olav
Published: November 2019

This paper describes a sophisticated yet easy-to-use digital solution with dashboards arranged with a browser-accessible graphical interface that provides E&P operators with real-time, end-to-end transparency of their operating assets, whether located offshore, onshore or a combination of both. This condition-monitoring application is called SOGO (Siemens Oil and Gas Offshore). In a reacent deployment, the solution is condition monitoring up to 1,300 equipment assets including approximately 60 asset types. It incorporates artificial intelligence, machine learning, and digital twin concepts to help operators implement predictive, condition-based maintenance and fleet management across single and multiple sites. Ample illustrations will enable readers to visualize the application’s use. Readers will gain insights to how digitalization can radically transform their management of operational assets to achieve quantum improvements in efficiencies, transparency, decision-support, and responsiveness, while gaining wholly new diagnostic, mitigation, and remediation capabilities.
Making the Case for Dry Low Emissions Technology in Offshore Oil and Gas Applications
Authors: Bulat Ghenadie , Rickert Matthew
Published: November 2019

This paper focuses on the use of gas turbines (GTs) equipped with Dry Low Emissions (DLE) combustion technology in offshore oil and gas (O&G) applications. The paper presents information from offshore GT units and discusses improvements in operational reliability and fuel flexibility that have been achieved as a result of extensive testing on combustion systems. These improvements, coupled with significant NOx emissions reductions have made DLE gas turbines a viable alternative to non-DLE gas turbines, the latter of which have traditionally dominated the offshore market.

Field operating data will be presented on three lightweight industrial gas turbine models that have been offered with DLE as a standard configuration for over 20 years. Currently, 394 units operate in offshore applications. The DLE configuration has accumulated more than 22 million hours of service. The paper outlines the performance, reliability, and fuel profiles of the current offshore fleet, confirming that DLE combustion systems have now achieved reliability on par with non-DLE combustion systems.

A brief review of the testing experience proving the reliability and the range of fuels that can be burned within the DLE system will also be discussed in the paper.
Full-Automated Managed Pressure Drilling System Facilitates Unlocking Unconventional Gas Reservoir Potential in United Arab Emirates
Authors: Al Hadidy Khaled , Hamdy Ibrahim , Al Samahi Musabbeh , Hadj-Moussa Ayoub , Saleh Mujahed
Published: November 2019

Drilling wells in potential Unconventional gas reservoirs is coupled with challenges that expose these wells to costly failures. Elevated formation pressure and pressure ramps create uncertainty in determining the mud weight to use in drilling UC wells. Also, various sources of gas influxes create confusion in analyzing well control situations and prolong handling kicks.

This paper details the approach taken on an unconventional exploratory HPHT gas well onshore Abu Dhabi to mitigate these drilling hazards.

After a thorough analysis of the drilling challenges that were encountered on a previous well drilled in the same field (Well-A), the drilling team has identified Managed Pressure Drilling (MPD) a key enabling technology that can help in mitigating the drilling hazards of (Well-A). A task force was formed with the MPD service provider to plan the MPD system deployment on Well-B. The task force came up with a detailed MPD program to address the anticipated drilling challenges in Well-B with multiple scenarios mitigation plan.

MPD goal was to identify the right mud weight to drill the reservoir section, handle gas influxes and analyze the sources of the gas at surface to minimize well shut-in time and to enhance the overall safety of the operations by providing close-loop drilling.

Drilling the reservoir section utilizing MPD has resulted in avoiding unnecessary well shut-in and raising the mud weight. Gas influxes were handled utilizing a dedicated MPD Mud Gas Separator (MGS) instead of the rig’s one, this approach enabled smoother drilling and coring operations. The pilot hole reached the planned Total Depth (TD) across high pressure ramps, coring program was implemented without interruption and full cores were recovered as planned. Non-Productive-Time (NPT) was reduced by 60% comparing to Well-A and no well control events occurred, saving cost and building confidence in the system. Therefore, a decision was made to continue utilizing the MicroFlux MPD system in the horizontal section of Well-B.

The MPD implementation on Well-B proved that the reservoir pressure was less than anticipated by 3 ppg, thus the 15K psi BOP was not required as thought previously, resulting in reducing the drilling cost of the consecutive wells. The impact of using a lower mud weight is believed to have a positive impact on the reservoir productivity.

This paper show-cases the utilization of the Full-Automated MPD on well-B as the first application of this technology in ADNOC onshore fields. It sets a stepping-stone for future implementations and capture important lessons learned and best practices that will certainly facilitate the development of Unconventional resources fields in the United Arab Emirates. Even more, the knowledge management process applied on Well-B can support tackling similar drilling challenges in mature fields.
Optimizing Completion Parameters via a Synthetic Catalog
Authors: Zheng Xinquan , Liu Yuan , Huang Yongjie , Enkababian Philippe
Published: November 2019

Unconventional resource development is increasing quickly in many places worldwide. For unconventional resources, multistage completions play a key role for both reservoir performance and well economics, which makes completion optimization a critical technical and commercial decision. This work integrates the reservoir modeling, fracture simulation, production forecast, and synthetic data pool generation via Monte Carlo methods, and it simplifies the final optimization process into a selection from multiple options.

There are many approaches used to optimize completion parameters in shale gas development in the Sichuan basin. Although a trial and error method may work well with an adequate number of wells, this approach is not efficient with few wells because it would take many years to optimize the drilling and completion strategy. Also, such an approach may produce ambiguous results related to high uncertainty due to drilling quality and completion inconsistencies.

An innovative workflow is defined in this work that combines reservoir modeling, fracture network simulation, production matching, regression analysis, and Monte Carlo methods. The procedure begins with modeling of the reservoir using the proper geological environment and reservoir properties. Based on this model, the hydraulic fracture network is simulated with varied compl etion parameter sets, including fluid volume, proppant volume, perforation spacing, and stage spacing. Production forecasting is then performed for each of the fracture network simulations, and the result is matched with previous offset well performance. Regression analysis is used to simplify the relationships between the input (completion parameters) and the output (production results). Finally, based on the regression results, a Monte Carlo method is used to generate a large number of input and output pairs creating a type of synthetic completion choice catalog. This catalog provides a pool of completion options, effectively reducing the optimization process to a choice of the best fit-for-purpose options.

A synthetic model based on Sichuan shale gas is used in this study to validate the workflow on a single- well basis. It successfully produced many synthetic simulation results. With the large number of completion parameters—production result pairs—it is easy to filter the results and identify which combinations are preferred in terms of cost and production. This work also demonstrates that optimization is subject to the definition of purpose and duration of the objectives, which can be used as an important evidence to support different strategies.