bluuebunny/crossref_metadata_2025_split huggingface dataset:
Reservoir Characteristics and Resource Potential of Oil Shale in Sultani Area, Central of Jordan
Authors: Li Ke , Samara Hanin , Wang Xuan , Jaeger Philip , Ganzer Leonhard , Wegner Jonas , Xie Lin 
Published: November 2019

Abstract
Oil shale is the most abundant fossil energy resource discovered in Jordan. The objective of this paper is to investigate reservoir characteristics and evaluate the resource potential of the Sultani oil shale deposit in central Jordan, based on their mineral composition, geochemical characteristics and reservoir microstructures.
The samples used for this study were taken from the outcrop in Sultani deposits, South-East of Al-Karak city adjacent to the desert highway. The collected samples were cleaned and made into powder sample, kerogen sample, thin section sample, and ion beam polishing sample. The powder sample was analyzed by X-Ray Diffraction and Organic Carbon Analyzer to clarify the mineral composition and TOC value. The kerogen samples were tested for evaluate the kerogen type and maturity of organic matter. The thin section and ion beam polishing sample were examined by Optical Microscope and Electron Backscattered Diffraction to observe reservoir microstructures. The Sultani shale is formed by various types of minerals, the majority composition is 67.25% calcite and 18.38% quartz, with little apatite, dolomite, and pyrite. The geochemical test shows that: The Kerogen type is dominated by type II1; the Sultani shale can be burned directly and continuously in the air, due to it contains a large amount of organic matter, TOC average value is 14.82%; the value of equivalent vitrinite reflectance is between 0.55% and 0.67%. The Sultani shale is high-quality source rock but with low maturity. Based on Optical Microscope and EBSD result, the micrite (calcite grain size&lt;0.004mm) constitute Sultani shale. Normally, the reservoir should have extremely low porosity, but there is an amount of foraminifer shell fossil which forms the pore structure. The remarkable thing is that the fossil pore have large pore volume and it is poorly connected to its neighbor, the hydrocarbon reserve in the isolated pores.
The Sultani shale is tight reservoir (large pore volume, but poorly connection) with economically attractive resource potential. However, there will be difficult for exploitation, due to it’s specially pore structure. Acid fracturing is feasible technology to connect the isolated fossil pore, thus significantly increase oil production. The Sultani shale can also be burned directly for power generation and get the lime product at the same time, Surface mining is also feasible exploitation patterns.
Lessons Learnt from the use of a Quantitative Risk Based Approach to Manage SIMOPS Activities
Authors: Ignatius Prashanth , Al Hammadi Saud , Basile Girolamo 
Published: November 2019

Abstract
A number of major process industry accidents have involved SIMOPS. Company conducted construction (modifications) activities inside its own plant during the period from November 2017 to May 2018. The Group Risk Acceptability Criteria Guidelines have been defined by Company for the purpose of providing Senior Management with quantitative information about the risk profile during SIMOPS activities and to help them in taking informed decision about the execution strategy to ensure safe operations.
A DNV-GL Phast based model of the plant has been used to assess the risk level. Using Group Risk guidelines for On-site personnel based on the FN (Frequency – Number of Fatalities) Curve, Company evaluated and compared several SIMOPS options prior to the actual works to identify the optimal manning level and schedule to ensure the overall Group Risk laid in the ALARP region.
The quantitative risk assessment served as a tool to derive the optimal manning levels and shutdown schedule during the SIMOPS activities. The manning levels were controlled through additional administrative measures to ensure its implementation.
Moreover, the overall SIMOPS Risk (FN Curve) for the current activity was compared with the risk undertaken during similar previous activities conducted in 2015 and 2016. After the successful completion of the activities, the Risk Assessment was updated to take into consideration the actual manning and schedule inputs for the Pre-Shutdown and Partial Shutdown phases. The actual overall Group Risk (including Pre and Partial Shutdown) was within the Company Group Risk Acceptability Criteria.
Additionally, valuable Lessons Learned were identified such as purging (with inert gas), rather than just the depressurization of the equipment in the units during Shutdown, can contribute to a significant risk reduction.
This paper presents a novel approach to evaluate SIMOPS risk on personnel using a Group Risk criteria based on FN Curve. This provides an additional re-assurance to stakeholders involved in the activity to take informed decisions based on a quantitative risk analysis rather than a qualitative assessment.
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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.