bluuebunny/crossref_metadata_2025 huggingface dataset:
An Effective Economic Approach for Confirming Bypassed OilWith the Use of a New Generation Slim Pulsed Neutron Logging Tool – A Field Case from the Gulf of Thailand
Authors: Vimolsubsin Pojana , Teeratananon Wirot , Chigbo Ikenna , Lerdsuwankij Thiti , Millot Pascal
Published: November 2019

This paper presents the successful application of a new-generation slim pulsed neutron logging tool for identification of bypassed oil in the Nong Yao field. The field comprises of different small pools of oil developed with horizontal wells. The wells are drilled with long lateral sections to increase the drainage area in an attempt to increase sweep efficiencies. However, the sweep efficiencies remained uncertain given reservoir heterogeneity and the nature of water encroachment into the wells.

Reservoir saturation monitoring through tubing is usually required for an effective reservoir management program in such a mature field, and a cost-effective method for future opportunity identification. The traditional slim pulsed neutron logging (PNL) tools often provided inconclusive results especially when deployed in complex completion conditions. A new-generation slim pulsed neutron logging tool, which provides high-resolution spectroscopy with a much-improved accuracy and precision was investigated and introduced. This tool delivers self-compensated sigma and neutron porosity measurements in a wide range of conditions, including complex completions and with varying amount of gas in the wellbore or annulus.

This new PNL tool was run in the Nong Yao field in December 2017 with the objective to prove the remaining oil at the top of a reservoir. The objective was to acquire data in GSH (sigma, fast neutron cross section, Porosity) and IC (spectroscopy) modes in 8-1/2" hole with conventional completion (7" casing + 2-7/8" tubing). Despite challenging borehole fluid conditions, the data acquired confirmed remaining oil in the reservoir and a new well drilled in 2018 targeting this bypassed oil is currently producing with very good oil production.

This successful implementation of PNL in 2017 led to the adoption of the tool as a good alternative for confirming bypassed oil in the Nong Yao field. This strategy has been adopted for well target validation and horizontal well placement to support the 2019-2020 infill drilling campaigns.

In December 2018, this tool was run again in three selected candidate wells to prove the remaining bypassed oil and oil saturation away from currently producing wells. The results acquired in all three cases showed clear oil/water contact movement and sweep where present, confirming sufficient remaining oil volume to justify the drilling of new infill wells to develop these volumes during the 2019-2020 infill drilling campaigns.

The new generation PNL tool provides a low-cost alternative for effective reservoir depletion monitoring. Proper reservoir management, additional opportunity identification, and infill drilling target optimization are all benefits that can accrue from accurately locating bypassed oil. Field development plans can then be further optimized, resulting in increased asset value.
Accurate Modeling of Relative Permeability Hysteresis in Water Alternating Gas Experiments
Authors: Aghabozorgi Shokoufeh , Sohrabi Mehran
Published: November 2019

The saturation history dependent relative permeability (kr) data have been reported frequently in the laboratory investigations. Accurate estimation of kr data with hysteresis effects is crucial, specifically in Water Alternating Gas (WAG) injection which involves a sequence of drainage and imbibition cycles. Although there are a few methods to model the hysteresis effects in three-phase systems, the predicted values are still not adequate to simulate the hysteresis observed in experiments.

In this study, a generalized three-phase hysteresis model was developed to simulate the observed hysteresis in the WAG experiments performed at Heriot-Watt University. It is discussed that the use of Land trapping coefficient in the hysteresis models is doubtful since it originates from the observed behaviour in two-phase systems which reach residual saturations. Hence, the new hysteresis model is developed based on innovative techniques to predict the oil and water saturation at the end of each injection cycle. Moreover, in the developed model, the formulations for estimation of hysteresis in water and gas kr data are updated to capture the observed behaviors in WAG experiments.

The suggested hysteresis model was evaluated by comparing the simulation results with the available experimental data. The results showed that the developed model is able to simulate oil, water and gas production more accurately. Based on the results, the model can simulate the pressure behaviours observed in the experiments with dominated hysteresis. In addition, the developed model can predict the oil, water and gas saturations at the end of each cycle with higher accuracy compared to the available methods in the literature.

The significant impacts of the hysteresis phenomenon on designing the best WAG injection scenario require a reliable hysteresis model for performing accurate reservoir simulations. The use of the suggested model elevates the accuracy of any feasibility analysis performed to evaluate the WAG injection scenario.
Improved Reservoir Characterization Through Rapid Visualization and Analysis of Multiscale Image Data Using A Digital Core Analysis Ecosystem
Authors: Chandra Viswasanthi , Tallec Gwenole , Vahrenkamp Volker
Published: November 2019

Efficient integration of multiscale image and petrophysical data is becoming increasingly important to tackle emerging reservoir characterization challenges associated with complex carbonate and unconventional reservoirs. In this paper we illustrate an integrated digital rock physics and petrophysical data analysis methodology empowered by a digital core analysis ecosystem, for defining reservoir rock types and flow units in a micritic carbonate formation. We apply the methodology to 35 meters of cored well data acquired from the Late Jurassic Upper Jubayla Formation, equivalent to the lower Arab-D reservoir in Saudi Arabia. Pre-processing, segmentation and digital rock physics calculations are performed using whole core computed tomography (CT), plug micro-CT, thin-section micrographs and scanning electron microscopy data. Further whole core CT data analysis includes generation of mean intensity and heterogeneity logs. The digital rock ecosystem is applied to these multiscale image data and to spatially correlate with petrophysical well logs. The unique whole core CT processing step in the workflow allows the core barrels to be intelligently removed, and all the cores to be stitched together regardless of the total size of data. We thus access the full advantage of 3D whole core CT data that provides significantly high vertical resolution of rock properties in the well interval. Furthermore, the live ecosystem enables the continuous integration of image and petrophysical data as they become available over the duration of this study. Results from digital image analysis reveal the micro- and macro-pore types and their connectivity across multiple scales. Combined with plug and thin section data, log interpretation and digital image analysis, these pore types are upscaled into well log scale through texture-based rock-typing. The digital core analysis ecosystem we employ in this study has a unique capability of visualizing and analyzing large volumes of image and petrophysical data, allowing a novel method for rock-typing. The proposed methodology is scalable to data sets consisting of many wells, thus making it a valuable tool for accurate characterization of complex carbonate and shale reservoirs, which are becoming increasingly reliant on high resolution imaging techniques for pore space characterization.
Unsupervised, Deep Learning-Based Detection of Failures in Industrial Equipments: The Future of Predictive Maintenance
Authors: Lygren Sture , Piantanida Marco , Amendola Alfonso
Published: November 2019

Objective/Scope

Despite tremendous efforts, AI-based predictive maintenance is still not fully exploited in Oil&Gas plants worldwide. The reason mainly relies on the fact that predictive maintenance algorithms need many examples of failures to be trained on, and this is not always the case. For this reason, we developed an efficient unsupervised approach for predictive maintenance, based on deep learning algorithms and applied successfully to predict and anticipate the failures of a coalescer of an Eni's offshore plant.

Methods/Procedures/Process

Our method is based on a Recurrent Neural Network (RNN) autoencoder architecture, coupled with clustering algorithms. The RNN is based on a combination of two algorithmic steps, respectively called encoder and decoder. The encoder reads multivariate chunks of data and summarizes them in a vector of fixed length, named context vector. Then, the decoder brings this context vector and reconstructs the input signals. Once the reconstruction error is minimized, we cluster context vector by choosing an optimal number of clusters and associating them to the operating conditions of the equipment, in particular by distinguishing ‘healthy’ from ‘faulty’ states.

Results/Observations/Conclusions

We applied the aforementioned workflow to distinguish the operating conditions of a small equipment in an Eni's offshore plant. This equipment, an electroastic coalescer, suffered repeated troubles during the first phases of plant start-up. We picked up all the sensor measurements available for the coalescer (pressures, levels, temperatures) with very tight sampling (10 seconds resolution) and trained the RNN architecture on 9 months of data. After the application of a suitable clustering method on the context vector minimizing reconstruction error, we were then able to detect up to 5 different operating conditions of the coalescer, associating them to healthy and faulty states of it. In particular, the method was able to authomatically cluster the failures periods of the coalescer, with an advance of around 4 hours before the failures occurred.

Novel/Additive Information

‘Effective unsupervised learning – learning without labelled data – remains a holy grail of AI’ (Andrew Ng, WIPO Technology Trends 2019, Artificial Intelligence). We tried to do a step forward in the application of unsupervised approaches to predictive maintenance of industrial equipments by developing an innovative deep learning based method and applying it to a coalescer of an Oil&Gas plant, getting results that are very promising for massive, large scale application in real production settings.
Innovations in 3D Finite Element Assessments of Well Integrity — A Case Study for an ADNOC Onshore Field
Authors: Al Hosani Mariam Ahmed , Masoud Rashad Mohamed , Al Beshr Huda Abdullatif , Latif Mohd Anwar , Al Hammadi Shamma Jasem , Khalil Ihab Nabil , Al Bairaq Ahmed Mohamed , Al-Ameri Ammar Faqqas , Nasreldin Gaisoni , Ni Qinglai , Rodriguez-Herrera Adrian , El Mubasher Husham Kamal , Corona Mauricio , Sinha Ravi Kumar , Subbiah Surej Kumar , Hussein Assef Mohamad , Karamalla Babikir Mubarak
Published: November 2019

The growing appreciation of the effects of production-induced stress changes on reservoir performance has concentrated the minds of many people on the potential value of using geomechanical modelling to predict and quantify these effects for making life-of-reservoir decisions—relating to compaction mitigation and completing new wells.

This paper is concerned with well integrity analyses for compacting reservoirs—focusing specifically on a new area of predictive geomechanical modelling realised using the finite element method. The innovative workflow presented offers significant improvements and, for the first time, captures some of the realities of the construction process. It takes into consideration both the formation and completions by integrating 3D near-wellbore geomechanical modelling with cementing simulations and casing integrity analyses. Specifically, casing eccentricity and cement contamination data are taken from numerical cementing simulations carried out to re-create the conditions in wells with different trajectories. Moreover, formation mechanical properties, pore pressure and stress states from the time of drilling till the end of a simulated production schedule are taken from a calibrated field-scale geomechanical model and subsequently used to create high-resolution 3D near-wellbore geomechanical models.

The case study presented in this paper concerns a giant onshore field with multiple stacked reservoirs—containing a variety of hydrocarbons and experiencing different levels of depletion. The main interest is in conducting comprehensive casing and cement integrity assessments—particularly for wells located in compacting reservoir zones. A persistent challenge for geomechanical modelling and prediction is the availability of calibration data. This paper reduces uncertainty by presenting results concerning sensitivity analyses for a variety of completion conditions—including different levels of casing eccentricity, different degrees of cement contamination and different extents of casing corrosion.
Advanced Reservoir Characterisation of Meandering Fluvial Environment, 3D Modelling Study Offshore Malaysia
Authors: Mohamad M Naim , Rahman M Zahari , Nourreddine Benayad , Yakup M Hamzi , Sedaralit M Faizal
Published: November 2019

Thorough reservoir modeling studies have been performed for field ABC, however there are still challenges to be addressed in modelling of some specific sand reservoir depositional systems i.e. meandering fluvial reservoirs (point bars and crevasse splays). The current modelling approaches especially for fluvial reservoirs are mainly controlled by wells and have contributed to uncertainties in lateral variation based on geostatistic (variograms etc) between and away from well control. Moreover, the existing modelling approach is using sixth to fifth order (lower order) hierarchical architecture elements and this project further refines the model up to third order (higher order) which enables capturing lateral accretion of point bars.

Advanced fluvial workflow (AFW) have been developed to improve the understanding of the reservoir architecture of fluvial reservoirs. It comprises of three main steps which are, first, details study on fluvial reservoir sedimentology characteristics derived from core analysis and literature. Second, qualitative geophysical study and interpretation derived from seismic dataset. Third, integration between the first and second steps into a three dimensional (3-D) reservoir model.

As a result of AFW implementation in field ABC, this has led to better representation of the reservoir heterogeneities, more accurate STOIIP assessment, improved history matching quality index (HMQI) and enhanced subsurface risks and uncertainties understanding. This enable optimization of future field development plan such as infill well reactivation, water flood and chemical enhanced oil recovery (EOR). The AFW is a robust modelling method that can be used in any reservoir modelling platform (PETREL, CMG, RMS, TNAV) with multiple realizations capability using automated workflows.
Natural Language Understanding for Safety and Risk Management in Oil and Gas Plants
Authors: Milana Diletta , Darena Maria Stella , Bettio Nico , Cerruti Chiara , Siliprandi Guido , Fidanzi Alessandra , Cerioli Paolo , Silvestri Giacomo , Tarasconi Francesco , Caserio Matteo , Botros Milad , Gabrielli Maria Luisa
Published: November 2019

In Oil and Gas, technical staff is daily involved in critical activities. Safety is therefore a key priority, even more so with frontier and continuously-updating technologies acting as a fundamental part of the transformation of the traditional industrial processes. While safety reports and investigations have long been adequately stored and continuously monitored by expert professionals, Artificial Intelligence applications to natural language now provide the opportunity to develop a decision support system capable of extracting insights, predicting the risk of future operations, performing scenario analysis and prescribing risk mitigation actions on massive amounts of data. In this work, we used an Open Innovation approach to develop a Safety Pre-Sense system, leveraging Machine Learning and Natural Language Processing techniques as well as incorporating multiple different (and often unexpected) sources of information.

Starting from standard Natural Language Processing tasks, we leverage linguistic patterns to build binary Document-Term Matrices. Operating on these Matrices, we implemented a Domain Keyword Extraction algorithm to extract words (or multi-words) that have high specificity.

Our pipeline also provides a language-agnostic method to detect similarities between documents written in different languages and cluster them accordingly, in order to obtain clear descriptors that can be used to understand their meaning. To do so, we map our text in a high-dimension vector space where we apply cluster analysis to group documents that are semantically close into consistent and multilingual groups. We then extract, for each language, a list of domain keywords that characterize every cluster.

Next, we identify similarities in the data in a completely data-driven manner, with the objective of extracting correlations between event features (such as geographical location and cause or type of event). As a result, we extract new aggregations of complex items such as severe Accidents or Work Processes. We also demonstrate how Correspondence Analysis and Pattern Mining algorithms are able to extract and visualize correlations between topics and events, leveraging a dynamic Qlik dashboard.

Finally, we point at additional sources of information, both internal and external to our company, that can be used to enhance our analysis.
Application of Machine Learning Tool to Separate Overlapping Fluid Components on NMR T2 Distributions: Case Studies from Laboratory Displacement Experiment and Well Logs
Authors: Rojas Pedro A. , Rincón Manuel , Netto Paulo , Coutinho Bernardo
Published: November 2019

Fluid typing, meaning fluid identification and quantification of each phase, is a significant challenge in NMR data postprocessing, particularly when the fluid spectral distributions overlap in one-dimension (T1, T2 spectra) or two dimensions (T1T2 or DT2 maps). Typical examples are extra-heavy oil and clay-bound water (CBW), heavy oil and capillary-bound water (BVI), free water and light oil or light oil-water and oil-base mud filtrate (OBMF). In these cases, technical limitations in data acquisitions and constraints in the inversion algorithms result in poor spectral resolution for those fluids with very similar physical-chemical properties. This makes very difficult the interpretation of NMR measurements from the laboratory as well downhole.

We present two case studies: one focused on determining water saturation (Sw) in core samples in a water-oil displacement experiment in the laboratory; the second is about determining the permeability by identifying OBMF from an NMR well log in a medium to light oil-bearing formation. In both cases the targeted fluid component was determined using blind source separation based on independent component analysis (BSS-ICA), which is a machine learning tool capable of separating spectral T2 components (sources) given their statistical independency.

The results from the displacement experimental show a high correlation (R2 higher than .85) between saturation from the BSS-ICA derived water component and the estimated value from known injected water volumes. In the well log case, the results show that the presence of OBMF and its volume are a good indicator of rock quality of the reservoir layers, as confirmed from several core measurements. Beyond this, the OBMF obtained from BSS-ICA decomposition is used as a key variable in a newly proposed permeability equation. After core calibration the OBMF-based permeability was found to be more representative than the permeability derived from the widely used Timur-Coates equation.
Assessment of Biodegradable Diverter for Acid Fracturing of Highly Fractured Formations
Authors: Al-Hulail Ibrahim , Eoff Larry , Anazi Mashhoor
Published: November 2019

Many application and operational methods have been developed for applying carbonate matrix acidizing to successfully stimulate heterogeneous and long horizontal openhole zones. These methods have also been implemented during acid fracturing to various degrees of success. This paper discusses in detail the laboratory assessment of a biodegradable material for acid diversion in highly fractured formations.

Diversion in fracture acidizing is extremely challenging because of the high pumping rate, extreme pressures, and larger volumes of acid compared to matrix acidizing. To effectively stimulate natural or pre-existing fractured formations, the diverting agent should be able to bridge not only at the perforations, but inside the fracture system, too. Historically, several methods have been implemented for acid-fracturing diversion, such as ball sealers, viscous fluids, packers, etc., resulting in limited success in formations with natural or pre-existing fractures. This paper discusses the use of an acid diverter that consists of biodegradable particles with different sizes and hardness. The particle size ratios are specifically designed where large particles will bridge in the fractures while the smaller particles "nest" in the pore throat of the bridged larger particles. This leads to quick, efficient blockage of fractures and acid diversion.

The laboratory assessment of this biodegradable material was conducted at various temperatures up to 300°F and consists of (1) degradation in 3% KCl, live 15 wt% HCl, and spent 15% HCl, and (2) fluid loss using slotted disks at different diverter concentrations. The fractures were mimicked in the laboratory using a stainless steel slotted disk in a high-pressure/high-temperature (HP/HT) cell.

The dissolution rate of the particles was observed to be a function of time and temperature. The dissolution rate of the diverter was higher in water as compared to 15 wt% HCl acid. The stability of the biodegradable diverter was conducted at 300°F. The filter cake was stable up to 30 minutes when 1.0 ppt of the biodegradable diverter was used.

The results of this study indicate that the biodegradable diversion material can be used as an effective alternative diversion method to seal natural or pre-existing fractures.
Alternative Environmentally Friendly Solvents for Asphaltenes/Paraffins Removal from Oil Producing Wells
Authors: Al-Taq Ali , Alfakher Basil , Alrustum Abdullah , Aldarweesh Sajjad
Published: November 2019

Aromatic-based solvents, including benzene, toluene, xylene (BTX) and their derivatives have been successfully applied for asphaltene removal from downhole and surface facilities. These solvents are considered non-environmentally friendly due to their associated health and safety concerns including high toxicity, low biodegradability and low flash point. Currently, more attention has been given in the oil industry to develop environmentally friendly asphaltene solvents.

This paper examines several environmentally friendly solvents derived from natural precursors to dissolve asphaltene, wax and combined asphaltene/paraffin organic deposit. A group of plant-derived and terpene-based asphaltene solvents with flash points ranging from 50.5 to 136 °C was examined in this study. Extracted asphaltene from a crude oil and wax obtained from distillation were used to assess solvency power of these solvents. Solubility of organic deposits containing more 42 wt% asphaltene with associated paraffin was evaluated in these solvents. The performance of these solvents was examined as a function of soaking time and temperature. These environmentally friendly solvents showed comparable solvency power to toluene. The lowest flash point solvent exhibited the highest solvency power for asphaltene while the opposite relation was observed for the wax sample. The lowest flash point (50.5 °C) solvent was able to dissolve 91 wt% of the asphaltene sample after soaking for 2 hours at ambient temperature compared with the highest flash point solvent (136 °C), which dissolved only 7.4 wt% at the same conditions. For wax, the solvent with the second highest flash point (132 °C) was able to dissolve 97 wt% of the wax while the solvent with a flash point of 50.5 °C was able to dissolve 85.5 wt% at ambient temperature and after 2 hours. Some of the examined environmentally friendly solvents showed very high dissolution power to organic deposits composed of asphaltene and paraffin where a solubility of 96 wt% was obtained at 80 °C and after a soaking time of 6 hours. The paper will discuss these results in detail.
Lean Logistics for Green Field Exploration Activities
Authors: Niccolini Federico , Chisari Luca Antonio , Maggioni Bruno
Published: November 2019

The necessity to reduce the costs incurred in the oil and gas project lifecycle pushes the oil Companies to evaluate alternative strategies with respect to the standard logistic approach. A case study for offshore logistic optimization and cost reduction is presented for a remote (138 nm from the operating port) deep-water offshore green field exploration project in Morocco (2018).

Green field exploration represents a challenging context of operations for logistics due to limited availability of already contracted services and/or infrastructures and possible limited familiarity with O&G of the hosting country. Following main elements of cost saving and optimization opportunities have been identified and pursued: i) exploitation of synergies with other company operated projects, ii) drillship storage capacity exploitation, iii) redeployment of material to reduce company stock and improve spud-in availability, iv) circular economy, v) transportation fleet sizing optimization, vi) flexible services and prompt release after operations conclusion.

In presence of a drillship deployed for multiple back-to-back projects, the possibility to load the rig with required materials for the spud-in of subsequent projects represents an opportunity for overall logistic optimization. For the presented case study, the contracted drillship has been loaded at Cyprus, after relevant exploration, with redeployment materials and consumables requested for the spud-in of Morocco exploration. Demobilization from Cyprus has been exploited as mobilization for the Moroccan campaign with the drillship reaching the well site in a status of readiness for spud-in.

The materials availability at rig site form the initial project stages determined an optimization of the related transportation fleet and the reduction below 5,000 sqm of the required storage areas footprint within a port that showed limited availability to dedicate existing storage areas to O&G activities. The marine base setup has been further optimized by implementing flexible contractual solutions foreseeing prompt release of unused services.

Moving ahead of the standard interpretation of the drillship as only the final delivery point of the supply chain, a new approach has been analyzed and implemented to make efficient use of the drillship's storage capacity by the means of the identification and exploitation of operational logistic opportunities. Overall logistics costs incurred for the Moroccan exploration campaign were of 6.5 M$, meeting the target of capital exposure minimization before commercial discovery and the additional target to avoid binding agreements for infrastructure usage and/or refurbishment.
Challenges with the Mega-Projects Development in Russia and Opportunities for International Cooperation
Authors: Akimova Irina
Published: November 2019

Oil and gas industry in the Russian Federation contribute more than 40% of federal budget revenues and thus its so crutial to find best solutions to its main challenges along with new opportunities for growth. One of the main challenges for the Russian oil and gas industry right now is to maintain and even develop its hydrocarbon resourse base in the regard of sustainable development of the country and its regions.

According to the latest data of the State balance of natural resources of the Russian Federation from 01 of January 2016, during 140 years of oil and gas commissioning in Russia 3454 fields were discovered, among them 2462 oilfields, 572 oil and gas fields, 420 gas and gascondencate fields. Leading production area in Russia is Western-Siberian oil and gas megaprovince.
Static and Dynamic Characterization of a Tight Reservoir- A Case-Study from Onshore Abu Dhabi Field
Authors: Agnihotri Praveen , Pandey Vikram , Thakur Parmanand , Al Mansoori Maisoon , Rebelle Michel , Smith Steve , Bhatt Pranjal , Zhunussova Gulzira , Hassan Syed
Published: November 2019

Holistic assessment of project economics and subsurface characterization provides a framework to handle challenging reservoirs. Capturing ranked uncertainties based on their impact on the project and meticulous working towards de-risking the project is key for the success of the entire project. Committing increased production from the field is dependent on proper evaluation of the reservoir.

This paper reviews characterization of a tight reservoir deposited in the intra-shelf Bab basin during lower Aptian time. Initial stage reservoir characterization is critical in formulating reservoir development plan and estimating a realistic assessment of rates and volumes for the field.

The target formation is a low-permeability (average permeability 0.5 mD) heterogeneous carbonate reservoir sitting directly above and adjacent to a producing carbonate reservoir. It is essential to understand communication between the zones. The pilot well is drilled with 225 ft of conventional core and quad-combo logs. Advanced logs such as resistivity image, cross-dipole acoustic, nuclear magnetic resonance, vertical interference test (VIT), formation pressure (including pressure transient data), and fluid samples were acquired. The main objectives of the evaluation program were to determine the formation pressure, collect representative oil sample(s), conduct vertical interference tests between the sub-zones and collect appropriate data for geomechanical and rock-physics characterization.

Thorough pre-job planning and cross-discipline cooperation during the operation provided high fidelity log data and interpretation of the data into a coherent result. This included integration of image data with vertical interference tests from the wireline formation tester (WFT) where barriers were confirmed. In addition, NMR permeability was matched and calibrated using pretest mobility measurements and formation pressure data was combined with full waveform advanced acoustic processing to explain the communication between the upper target zone and the lower producing reservoir. Advanced acoustic analysis helped to fully characterize the target formations with stoneley permeability, azimuthal anisotropy, and presence of fractures.

This paper demonstrates the importance of multi-disciplinary team effort in characterization of challenging reservoirs. It highlights the importance of holistic planning before the execution phase, and keeping a focus on the larger goal while executing individual aspect of a complicated project.

Formation evaluation measurements have evolved over decades and occasionally it benefits the industry to provide a review of how the latest logging measurements fit together in an integrated manner, for successful evaluation of a challenging reservoir.
Application of CNN Deep Learning to Well Pump Troubleshooting via Power Cards
Authors: Zhou Xiangguang , Zhao Chuanfeng , Liu Xiaohua
Published: November 2019

Recent years have seen extensive applications of deep learning, especially in identification and analysis of images, audios and texts, but incipient applications in petroleum industry. Shapes of loops in power cards of a pumping unit are valuable indicators for pump troubles. These troubles may cause engineering accidents, increase operation costs and reduce operation efficiency. This paper applies image recognition technique based on Convolution Neural Network (CNN) to well pump troubleshooting via power cards.

Recent years have seen extensive applications of deep learning, especially in identification and analysis of images, audios and texts, but incipient applications in petroleum industry. Shapes of loops in power cards of a pumping unit are valuable indicators for pump troubles. These troubles may cause engineering accidents, increase operation costs and reduce operation efficiency. This paper applies image recognition technique based on Convolution Neural Network (CNN) to well pump troubleshooting via power cards.

Firstly, we establish mathematical models both for displacements of the polished rod clamp of a pump and for loads of the polished rod during a reciprocating movement, and preset input parameters corresponding to pump trouble types and severity levels. Ideal benchmarking power cards as the media for pump troubleshooting are generated by simulating complete pumping processes via running the mathematical models with the preset pumping parameters.

Secondly, we establish a power card classification model with the AlexNet method. Then we train it with the ideal benchmarking power cards to develop its function of pump troubleshooting and increase the classification accuracy. This model gains robustness and universality from manually presetting parameters for and full coverage of trouble types and severity levels.

Thirdly, we train the classification model with real power cards and obtain the preliminary classification results. A further training makes it more practical and applicable to local operations of pump troubleshooting. In the further training, we localize the ideal benchmarking power cards via manual inspection and local expertiseby adjusting the preliminary classification results honoring field expertise.

Finally, we randomly divide the localized benchmarking power cards into one training set and one testing set, and then train the classification model with the training set and then apply it to the testing set. The final classification results revealthe high accuracy and practicability of the classification model.

It is recommended that GPU should be used for calculation with the classification model to satisfy clients' requirements for higher speeds and efficiency. It provides a feasible method to exploit the potential value of oilfield data assets.

The work in this paper will function as a stepping stone in applying ideas, algorithms and models of artificial intelligence to more extensive and thorough aims.
Overcoming the Challenges of Designing, Developing, and Operating LNG Plants in an Arctic Environment
Authors: Wildman Steve
Published: November 2019

This paper focuses on cost-effectively designing, developing, and operating liquified natural gas (LNG) facilities in Arctic environments. It discusses the inherent challenges associated with engineering, constructing, and operating LNG infrastructure in harsh and highly remote conditions and outlines proven strategies for reducing risk. Specific areas that are discussed include the use of modular design techniques to mitigate overall project risk and drive efficient execution, as well as the deployment of digital tools/technologies for minimizing operating and maintenance costs and maximizing plant availability. For illustrative purposes, the paper discusses the strategies employed on two real-world LNG projects, both of which are located inside the Arctic Circle.
Correlated Friction Reduction and Viscoelastic Characterization of Utilizing the Permian Produced Water with HVFRs during Hydraulic Fracturing
Authors: Ba Geri Mohammed , Flori Ralph , Ellafi Abdulaziz , Noles Jerry , Essman Jacob , Kim Sangjoon , Alkamil Ethar H.
Published: November 2019

Hydraulic fracturing operation requires securing sufficient water resources to access unlocked formations. Successful treatment depends on the fracture fluids that mainly consists of water-based fluid with a low percentage of chemical additives around 1%. Therefore, the oil and gas industry are considered as the largest freshwater consumers by 3 to 6 million gallons of water per well based on a number of fracturing stages. As a result, the traditional water resources from subsurface and surface are getting depleted, and availability of freshwater is becoming more difficult with high cost due to continued demand. For example, operator companies in West Texas face many challenges, including a recent increase from USD 3 to 10 per m3 of freshwater. In addition, transporting process of the raw water to the fracture sites, such as Bakken has an environmental impact, and expensive costs up to USD 5/bbl, while costs of water disposal in range of USD 9/bbl.

This paper aims to study the produced water as alternative water-based fluid with high viscosity friction reducers (HVFR) to reduce environmental footprints and economic costs. To address utilizing produced water as an alternative capable water resource that may use during fracturing treatment, this research presents an experimental investigation associated with using the Permian high-TDS brine water with HVFRs. This work includes experimental research, case studies, and guidelines work on recent improvements on using HVFR to carry proppant and capture the optimum design in fracturing operations. Moreover, the research conducted scaled lab friction measurements that can in turn to be used to improve forecasting of frictions in the field, and therefore of expected surface treating pressures during fracture treatments. Evaluating pipe friction as a function of time to compare HVFRs efficacy in lab and field conditions as well as to predict maximum injection rate during a frac job is investigated.

The outcomes show that high-TDS Permian water with highest dosage of HVFRs had instantaneous pressure reduction effect in 10 seconds while low dosage of HVFRs had lost the effect slowly after 4 min. 30 sec. Also, the results of this study show that the variation of viscosity and pressure reduction at higher shear rate is small. The warm temperature helped rapid polymer dispersion and provided better environment to polymer hydration leads to rapid pressure reduction. Finally, successful implementation in Wlofcamp formation shows that the operation treating pressure reduced from 11,000 to 8,000 psi. The general guidelines obtained can promote the sustainability of using hydraulic fracturing treatment to produce more oil and gas from unconventional resources without considering environmental issues.
Modifying Control Logic to Improve Plant Availability
Authors: Al Dhaheri Abdullah Mubarak , Al Shamsi Abdulla Saeed
Published: November 2019

ADNOC LNG (Train 3) processes HP gas from ADNOC Offshore and the gas goes through a series of processes to remove sulfur & other impurities, gets dehydrated before it is processed to separate the various products and cooled to the prescribed temperature to enable storage and shipping. Apart from LNG, Propane, Butane, Paraffinic Naphtha and Sulphur are also produced as secondary products. The refrigeration of the gas is achieved in two systems namely Propane refrigeration and Mixed Component Refrigeration. Propane refrigeration loop is the heart of the total cooling process in which "Cold Box" is a set of exchangers in series.

The Challenge:

Cold box consists of 12 exchangers/evaporators in series and the process is highly interactive. Disturbance in level of one results in cascading effect on others.

It has been a challenge, during start-up, to stabilize the levels as we had to have substantial manual intervention. Any instrument fault or routine maintenance during normal operation required operator to take multiple level controls in manual to handle the situation.

This had resulted in delayed start-up and poor temperature profile of chillers.

Engineering and Operations teams jointly undertook an initiative to resolve this chronic problem, which was successfully demonstrated post-Train 3 T/A startup in May 2018.

It was jointly agreed to find a solution that would Minimize manual intervention during start-upMaintain stability during normal operationReduce manual interventions in case of instrument failure or calibration

Feed Forward Control Scheme

Implementation of a control scheme in DCS utilizing feed forward action from downstream controllers to upstream level controller. The solution was found simple and robust. Existing software tools available were used effectively to arrive at feed forward constants. Change was implemented during Train-3 Shutdown. A fall back strategy was kept ready – in case new scheme encountered unforeseen difficulties.

Business Benefit

Reduction in start-up time by around 1 Hr. will result in additional production of around 800 Tons of LNG per year which amounts to US$ 257K

Reduced probability of unplanned plant outages. Assuming US$ 1.2 M loss per unplanned shutdown reduction in loss per year comes to US$ 480 K
The DMX Protocol: A New Generation of Geology Driven 3D Discrete Fault and Fracture Network Modelling
Authors: van Dijk Janpieter
Published: November 2019

A new Protocol ("DMX") is presented for 3d DFFN (Discrete Fault and Fracture Network) modelling, a numerical code developed over the last 20 years in order to converge towards a more realistic Discontinuity (fault and fracture) Network representation in space. The protocol introduces the following new features: Fracture interaction, truncation, termination and cross cutting in 3d space based on newly designed collision algorithms and fracture propagation principles; Modelling at any scale range of unlimited basic 3d fracture shapes, specific 3d fracture morphology, and 3d fracture aperture types; A complete integration between classical geological/geomechanical drivers such as stress ellipse, fault zones with 3d slip vectors, and different fold models (axial plane, fold axis and bedding orientation conditioning), geological assembly modelling such as joint spacing and set dependency, offset/faulting, and probabilistic conditioning of any of the parameters and drivers. Examples of the application of the protocol are presented to illustrate few of the unlimited amount of combinations that can be generated in 3d space. Furthermore, an example of the complete flow chart of a calibration to real observed cases is provided. The protocol constitutes a complete game change and opens a range of technological challenges for the future applications in Mining, Civil Engineering and Conventional and Unconventional Oil and Gas Exploration and Production.
From Data to Actionable Insight: Monitoring Fugitive Methane Emissions at Oil and Gas Facilities Using Satellites
Authors: Gauthier Jean-Francois , Germain Stephane
Published: November 2019

Up until recently, the monitoring of greenhouse gases with satellites had been limited to a regional or global scale. Because of the low spatial resolution of scientific satellites looking at gases, attributing emissions to specific facilities had so far not been possible. GHGSat changed that narrative with its first satellite GHGSat-D in June 2016, the first and only in the world specifically designed to monitor emissions directly from industrial sites, with a spatial resolution of less than 50m. The system makes it possible for oil and gas companies to keep a frequent eye on their facilities scattered across vast areas at the lowest cost possible since all measurements are performed remotely with no need to access the sites.

We present recent single pass measurements taken with our demonstration satellite in the Short-Wave Infrared (SWIR) band, showing evidence of point source emission plumes at facilities such as underground coal mine vents and oil and gas facilities.

The lessons learned from GHGSat-D in the last three years making over 4,000 measurements at hundreds of facilities around the world have been incorporated into our second satellite scheduled for launch in August 2019. As a result, GHGSat-C1 is expected to improve on the performance of its predecessor by an order of magnitude. We will present some of the first results from this second satellite.

Finally, we introduce some of the innovative products and applications we are developing using analytics, artificial intelligence and machine learning to better serve our customers with actionable insight and optimize the operation of our system. The ability of the technology to work together with other sources of data (such as other satellites, drones or ground measurements) in an effective tiered monitoring system will also be demonstrated.
Improving Nmr Log Interpretation to Support Mature Field Development, A Case Study Offshore Abu Dhabi
Authors: Yousif Sharifa , Aboujmeih Hassan
Published: November 2019

Nuclear Magnetic Resonance logging measurements (NMR) provide detailed information about rock texture and pore distribution. The main objective of this study is to highlight a carbonate reservoir characterization example in a mature field, offshore Abu Dhabi; providing qualitative porosity, permeability and pore type classification in real time (while drilling), to support efficient field development decision making.

Different logging while drilling vendors tools (NMR-WD) operate at different concepts; some use the longitudinal relaxation time (T1) measurements, others apply the transverse relaxation time (T2). In this case, a low magnetic field gradient (T2) tool type was deployed in a tight formation horizontal oil producer. The well objective is to expose the maximum reservoir contact (MRC).

Primarily, the acquired (NMR) spectrum was used to deliver accurate total porosity, to compute Archie's water saturation. However, delivering a quantitatively reliable permeability become very challenging in the complex carbonate environment subject to study as it was well linked to (NMR) pore size distribution. At first, a standard (T2) cutoff value was applied. The computed (bulk irreducible water – BVI) was too low and hence the permeability was too high, resulting in inaccurate NMR interpretation. Next, a varying T2 cutoff – per zone was applied based on the changing spectrum profile itself. Finally, a Gamma Inversion technique by the service company was introduced to better quantify the different pore types and the corresponding permeability.

The (NMR) log analysis was validated with well core data in addition to production logging results. The data was applied to design the well stimulation and completion programs resulting in a healthy oil producer drain added to the asset.

Integration of Gamma Ray-resistivity-NMR and borehole image logs helped to consolidate the interpretation findings hence supporting decision making for mature field development.