bluuebunny/crossref_metadata_2025 huggingface dataset:
Monitoring Dynamic Water Injection to Improve Oil Recovery Efficiency
Authors: Al Hamad Mohammed , Zhang Ping , AlZoukani Ahmad , Altundas Bilgin , Abdallah Wael
Published: December 2021

Dynamic water, also known as smart water, injected at the end of conventional water flood by seawater, is known to show significant improvement in recovering additional oil. Different mechanisms have been proposed and lab measurements were conducted to understand the underlying process of additional oil recovery through dynamic water injection in lab conditions. In this work, we study the effects of different dynamic water injection scenarios on oil recovery in carbonate reservoirs based on reservoir simulations using representative fluid and rock properties with relative permeability curves obtained from core studies. To quantify the changes in measurable multiphysics properties due to dynamic water injection and reconcile multiphysics interpretation with additional oil recovery at field scale, a petrophysically consistent multiphysics effective property modeling is conducted. Based on the simulation results, dynamic water injection is shown to be effective in additional oil recovery at field scale post seawater injection. In addition, saturation changes caused by dynamic water injection result in detectable time-lapse contrast in the corresponding conductivity profiles, suggesting feasibility of the resistivity measurements to monitor dynamic water injection. This paper shows the advantages and benefits of petrophysically consistent multiphysics effective property modeling for a successful fluid monitoring design for quantifying the efficiency of dynamic water injection on additional oil recovery post seawater flood.
Multi-horizon Simultaneous Tracking Based on Dynamic Seismic Waveform Matching
Authors: Chen Maoshan , Wan Zhonghong , Wang Changhong , Liu Jingyan , Chen Zhaoqin
Published: December 2021

Due to the rapid increase in the amount of seismic volumes, the traditional seismic interpretation mode based on manual structure interpretation and single-horizon automatic tracking has encountered many challenges. The seismic interpretation of large or super-large 3-D seismic surveys is facing serious accuracy and efficiency bottlenecks. Aiming to the goal of improving the accuracy and efficiency of seismic interpretation, we propose a dynamic seismic waveform matching technology based on the sparse dynamic time warping algorithm under the guidance of the relative geological time volume theory, and realize multi-horizon simultaneous tracking based on the technology. Has been verified by a model and a real seismic volume, it can realize simultaneous horizon automatic tracking, full spatial tracking and high-density tracking, and can significantly improve the accuracy and efficiency of structure interpretation.
Reservoir Management Optimization Model Employing Combination Deterministic and Probabilistic Approach for Carbon Sequestration Storage and Utilization: A Case Study from East Natuna
Authors: Cherdasa Jeres Rorym , Ariadji Tutuka , Sapiie Benyamin , Siagian Ucok W. R.
Published: December 2021

East Natuna is well known for its huge natural gas reserves with a very high CO2 content. The appearance of CO2 content in an oil and gas field is always considered as waste material and will severely affect the economic value of the field. The higher the content, the more costly the process, both technically and environmentally. In this research, the newly proposed reservoir management approach called CSSU (Carbon Sequestration Storage and Utilization) method is trying to change the paradigm of CO2 from waste material into economic materials.

The novelty of this research is the combined optimization of deterministic and stochastic methods with the Particle Swarm Optimization (PSO) algorithm to answer complex and non-linear problems in the CSSU (Carbon Sequestration Storage and Utilization) method. The CSSU method is an integration of geological, geophysical, reservoir engineering and engineering economics with the determination of technical and economic optimization of the use of CO2 produced as working fluid in a power generation system that has been conditioned through an injection-production system in geological formations.

The CSSU research area is located in a sedimentary basin that has a giant gas field with 70% CO2 content. The Volumetric Storage Capacity for CO2 injection process in research area is 1,749.14 BCF or 94.01 MMTon which being calculated based on static modeling considering geological, geophysical and petrophysical aspects.

A combination of Compositional, Geomechanics and Thermal reservoir simulation model had been conducted to determines the Storage Injection capacity and later to prove the CSSU method in which CO2 fluids will be utilized as working fluid, 1 case was built using 2 Injection Wells and 1 CO2 fluid Production Well. The simulation results show with 1 production well the total of CO2 fluid injected from 2 Injection wells can almost double the injection total capacity up to 1,150 BCF. The utilization of supercritical CO2 fluid as working fluid can produce 55 – 133.5 MMBTU/Day or 0.67 - 1.63 MW from 1 production well for 25 years timeframe.

The CSSU method is optimized by deterministic and stochastic methods using the Particle Swarm Optimization (PSO) algorithm by looking the technical and economical aspects. The technical optimization aspect is being analyzed by electricity production versus well counts. The economical optimization is being analyzed by operational expenditure saving versus well counts and electricity produced versus NPV 10%. From both aspects the 4 injector wells case and NPV 200.00 MM US$ gives the most optimum result within technically and economically. The CSSU economic model proved with CSSU scheme the economical value is being increased by 57 MMUS$ after operating cost efficiency due to the electricity savings, 92 MMUS$ due to Carbon Trading which resulting the NPV 10% is 172.77 MMUS$.
Fractures and Flow Patterns Detection in Carbonate Reservoirs Using Intelligent Sensor Selection in a Deep Learning and Uncertainty Framework
Authors: Katterbauer Klemens , Marsala Alberto , Al Shehri Abdallah , Yousif Ali
Published: December 2021

4th Industrial Revolution (4IR) technologies have assumed critical importance in the oil and gas industry, enabling data analysis and automation at unprecedented levels. Formation evaluation and reservoir monitoring are crucial areas for optimizing reservoir production, maximizing sweep efficiency and characterizing the reservoirs. Automation, robotics and artificial intelligence (AI) have led to tremendous transformations in these areas, in particular in subsurface sensing. We present a novel 4IR inspired framework for the real-time sensor selection for subsurface pressure and temperature monitoring, as well as reservoir evaluation. The framework encompasses a deep learning technique for sensor data uncertainty estimation, which is then integrated into an integer programming framework for the optimal selection of sensors to monitor the reservoir formation. The results are rather promising, showing that a relatively small numbers of sensors can be utilized to properly monitor the fractured reservoir structure.
Novel Eco-Friendly Kinetic Hydrate Inhibitors
Authors: Xu Kui , Stewart-Ayala Jonathan , Jackson Steve , Hutchinson Benton , Sanders Christina , Jakubowski Wojciech , Jardine Joanne , Lehman Rose
Published: December 2021

Amid concerns over negative the environmental impacts of offshore chemicals, Baker Hughes explored new chemistries to develop environmentally friendly kinetic hydrate inhibitors (KHI). Our efforts were focused on improving biodegradability and toxicity of KHIs to meet environmental protection requirements, as well as mitigating challenges in field applications. A novel KHI design with branched polymers containing sugar alcohol ester groups as linkages, was proposed and synthesized. The new KHI polymer demonstrated > 20% biodegradability and >100 mg/L toxicity to seawater algae, and it also exhibited competitive or even better KHI performance to traditional non-biodegradable KHI products. Additionally, new KHI showed improved stability in water/brine at elevated temperatures as compared to traditional KHI products, which might mitigate concerns on polymer deposition at high temperatures.
Characterization of Tight Carbonate Reservoir by Using Nuclear Magnetic Resonance Log Analysis in the Bahrain Field
Authors: Al Saffar Rabab , Dowen Michael
Published: December 2021

The Bahrain Field (the "Field"), discovered in 1932, is an asymmetric anticline trending in a North-South direction of the Kingdom of Bahrain. It is a geologically complex field with 16 multi-stack carbonate and sandstone reservoirs, most of them oil bearing. The fluids varying from shallow tarry oil in Aruma to dry gas in the Khuff and pre-Khuff reservoirs. The Field has more than 2000 wells of which 90% have good quality log data.

The Ostracod and Magwa reservoirs are heterogeneous, layered tight reservoirs and have been on production since 1964. The Ostracod reservoir consists of very heterogeneous with limestone intervals intercalated between shale layers, with a total thickness of around 200 ft. The Magwa reservoir conformably underlies the Ostracod reservoir. The Ostracod averages 120 ft in thickness and is dominated by limestone with high porosity, low permeability, and variable water saturations. Core derived permeability measurements are usually less than 5 mD and porosities average 22%.

Production performance of individual wells is extremely variable and in many cases appears to be at odds with log-calculated saturations. Wells having good oil saturation often produce water and wells with low oil saturation produce high volumes of oil. Several studies have been conducted in an attempt to understand and resolve this.

The variability of oil saturation which has been mapped both laterally across the Field and vertically within wells, led to the question of what caused the variation in oil saturation. The variation is not a function of depth, which one might expect. Causes might include oil failure to migrate into certain reservoir compartments, a loss of the original charge to shallower reservoir or the oil charge been restricted by rock quality.

This paper attempts to address the variability in saturations seen across the Field and link known productivity to the Petrophysical interpretations. Nuclear Magnetic Resonance (NMR) logs had been employed in a targeted area of the Field in order to investigate rock quality in an attempt to explain the oil saturation distribution. A small NMR core study was undertaken in order to calibrate the NMR log response. The NMR data had been initially processed with what was considered a representative cut-off for Middle East Carbaonte rocks. This core study resulted in a surprisingly low series of T2 cut-off. The NMR logs were reprocessed with the more representative T2 cut-off. The resulting bound and free fluid fractions seemed to explain the observed well production.
A New Model for Predicting Liquid Loading in Shale Gas Horizontal Wells
Authors: Zhou Chao , He Zuqing , Chen Yashu , Wang Zhifa , Mulunjkar Amol , Zhao Weishu
Published: December 2021

Current critical flow rate models fail to accurately predict the liquid loading statuses of shale gas horizontal wells. Therefore, a new critical flow rate model for the whole wellbore of shale gas horizontal wells is established. The results of the new model are compared to those of current models through the field case analysis. The new model is based on the dynamic analysis and energy analysis of the deformed liquid-droplet, which takes into account the liquid flow rate, the liquid-droplet deformation and the energy loss caused by the change of buildup rate. The major axis of the maximum stable deformed liquid-droplet is determined based on the energy balance relation. Meanwhile, the suitable drag coefficient equation and surface tension equation applied to shale gas horizontal wells are chosen. Finally, the critical flow rate equation is established and the maximum critical flow rate of the whole wellbore is chosen as the criterion for liquid loading prediction. The precision of liquid loading prediction of the new model is compared to those of the four current models, including Belfroid's model, modified Li's model, liquid film model and modified Wang's model. Field parameters of 29 shale gas horizontal wells are used for the comparison, including parameters of 18 unloaded wells, 2 near loaded-up wells and 9 loaded-up wells. Field case analysis shows that the total precision of liquid loading prediction of the new model is 93.1%, which is higher compared to those of the current four models. The new model can accurately predict the liquid loading statuses of loaded-up wells and near loaded-up wells, while the prediction precision for unloaded wells is high enough for the field application, which is 88.9%. The new model can be used to effectively estimate the field liquid loading statuses of shale gas horizontal wells and choose drainage gas recovery technologies, which considers both the complex wellbore structure and the variation of flowback liquid flow rate in shale gas horizontal wells. The results of the new model fill the gap in existing studies and have a guiding significance for liquid loading prediction in shale gas horizontal wells.
Sensor Ball: Modernized Logging
Authors: Buzi Erjola , Seren Huseyin , Hillman Thomas , Thiel Timothy , Deffenbaugh Max , Bukhamseen Ahmed , Zeghlache Mohamed Larbi
Published: December 2021

The latest development in the electronics and manufacturing industry has enabled work towards the modernization of oil-field instruments. As a part of this trend, it is the time to invent and design small size oil-field instruments that could be much more practical to handle, easy to use, and less costly. High temperatures and pressures of the downhole environment make it very challenging to design and further develop such downhole instruments. To create such apparatuses, a thorough study of downhole conditions needs to be done upfront. This study will further help to define the design specifications and requirements. By targeting liquid wells in Saudi Arabia, we have overcome the challenges posed by the harsh downhole environment and managed to design and manufacture a hand-held device called ‘Sensor Ball’ and tested it in the field.
Satellite Fields Digitalization & ALS Optimization with EDGE & Advance Analytics Application
Authors: Johri Nitin , Pandey Nimish , Kadam Sanket , Vermani Sanjeev , Agarwal Shubham , Gupta Debashish
Published: December 2021

Data monitoring in remote satellite field without any DOF platform is a challenging task but critical for ALS monitoring and optimization. In SRP wells the VFD data collection is important for analysis of downhole pump behavior and system health. SRP maintenance crew collects data from VFDs daily, but it is time consuming and can target only few wells in a day. The steps from requirement of dyna to final decision taken for ALS optimization are mobilizing team, permits approvals, download data, e-mail dynacards, dyna visualization, final decision.

The problems with above process were: -

Insufficient and discrete data for any post-failure analysis or ALS-optimization Minimal data to investigate the pre failure events

The lack of real time monitoring was resulting in well downtime and associated production loss. The combination of IOT, Cloud Computing and Machine learning was implemented to shift from the reactive to proactive approach which helped in ALS Optimization and reduced production loss.

The data was transmitted to a Cloud server and further it was transmitted to web-based app. Since thousands of Dynacards are generated in a day, hence it requires automated classification using computer driven pattern recognition techniques. The real time data is used for analysis involving basic statistic and Machine learning algorithms. The critical pump signatures were identified using machine learning libraries and email is generated for immediate action. Several informative dashboards were developed which provide quick analysis of ALS performance. The types of dashboard are as below

Well Operational Status Dynacards Interpretation module SRP parameters visualization Machine Learning model calibration module Pump Performance Statistics

After collection of enough data and creation of analytical dashboards on the three wells using domain knowledge the gained insights were used for ALS optimization. To keep the model in an evergreen high-confidence prediction state, inputs from domain experts are often required. After regular fine-tuning the prediction accuracy of the ML model increased to 80-85 %. In addition, system was made flexible so that a new algorithm can be deployed when required. Smart Alarms were generated involving statistic and Machine Learning by the system which gives alerts by e-mail if an abnormal behavior or erratic dynacards were identified. This helped in reduction of well downtime in some events which were treated instinctively before.

The integration of domain knowledge and digitalization enables an engineer to take informed and effective decisions. The techniques discussed above can be implemented in marginal fields where DOF implementation is logistically and economically challenged. EDGE along with advanced analytics will gain more technological advances and can be used in other potential domains as well in near future.
Annual Work Plan Execution Performance Using an Automated Production Forecast Tool
Authors: Nuñez Gustavo , Tellez Camilo , Florez Fabian , Gallegos Johanna , Eremiev Francisco , Triviño Diego , Vallejo Renato
Published: December 2021

Shaya Consortium ramped up its production from 60 KBOPD to almost 85 KBOPD as a result of an agile execution of its Field Development Plan, made of infill drilling, workover interventions, and full-field expansion of waterflooding. This combined activity made the planning process very complex and dynamic due to the high volume of operations and scenario evaluation. Additionally, the consortium was requested to provide a weekly production forecast to its major stakeholders highlighting all deviations from the original execution plan and remedial activities to come back on track. The proposed application tool has simplified and automated the forecasting processes using short-term updates of the executed activities from field reports, current well status, planned workover interventions, and new wells drilling schedule. Any deviation of the Annual Work Plan due to schedule variance or well performance is automatically adjusted by the tool, creating a new forecast to End-Of-Year or Quarter even Weekly, thus, reflecting the impact on the estimated recoverable volumes.

The tool pulls information from different sources and consolidates them in a single unified environment, not only for forecasting but also as a visualization and analysis tool. Furthermore, it has several modules to facilitate the control of official type curves, scenario profiles for the Annual Work Plan, and it is fully linked to key corporate applications.

This paper presents the development of a production forecasting tool that introduced a new way of working within the Shaya Production Team by improving activity scheduling and overcome underperforming new wells, keeping the operations team informed to facilitate the production management.
Large Scale Placement For Multilateral Wells Using Network Optimization
Authors: AL-Qahtani Ghazi D. , Berlow Noah
Published: December 2021

Multilateral wells are an evolution of horizontal wells in which several wellbore branches radiate from the main borehole. In the last two decades, multilateral wells have been increasingly utilized in producing hydrocarbon reservoirs. The main advantage of using such technology against conventional and single-bore wells comes from the additional access to reservoir rock by maximizing the reservoir contact with fewer resources. Today, multilateral wells are rapidly becoming more complex in both designs and architecture (i.e., extended reach wells, maximum reservoir contact, and extreme reservoir contact wells). Certain multilateral design templates prevail in the industry, such as fork and fishbone types, which tend to be populated throughout the reservoir of interest with no significant changes to the original architecture and, therefore, may not fully realize the reservoir's potential. Placement of optimal multilateral wells is a multivariable problem, which is a function of determining the best well locations and trajectories in a hydrocarbon reservoir with the ultimate objectives of maximizing productivity and recovery. The placement of the multilateral wells can be subject to many constraints such as the number of wells required, maximum length limits, and overall economics.

This paper introduces a novel technology for placement of multilateral wells in hydrocarbon reservoirs utilizing a transshipment network optimization approach. This method generates scenarios of multiple wells with different designs honoring the most favorable completion points in a reservoir. In addition, the algorithm was developed to find the most favorable locations and trajectories for the multilateral wells in both local and global terms. A partitioning algorithm is uniquely utilized to reduce the computational cost of the process. The proposed method will not only create different multilateral designs; it will justify the trajectories of every borehole section generated.

The innovative method is capable of constructing hundreds of multilateral wells with design variations in large-scale reservoirs. As the complexity of the reservoirs (e.g., active forces that influence fluid mobility) and heterogeneity dictate variability in performance at different area of the reservoir, multilateral wells should be constructed to capture the most productive zones. The new method also allows different levels of branching for the laterals (i.e., laterals can emanate from the motherbore, from other laterals or from subsequent branches). These features set the stage for a new generation of multilateral wells to achieve the most effective reservoir contact.
Field-Scale Modeling of Smart Completion Tools for Optimum Recovery
Authors: Chen Zhen , Shaalan Tareq , Qahtani Ghazi , Manzoor Shahid
Published: December 2021

Flow control devices (FCDs) like inflow control devices (ICDs) and interval control valves (ICVs) (i.e., equalizer) have increased applications in both conventional and unconventional resources. They have been used to mitigate water or gas coning problems for mature fields in conventional reservoirs, to alleviate premature water breakthrough in naturally fractured reservoirs, and to optimize the steam distribution in heavy oil reservoirs. There have been increased trend in using FCDs in the real field. Previously, complex well models have been implemented in a large-scale parallel reservoir simulator by Tareq et al. (2017). The implementation can simulate an intelligent field contains tens to hundreds of multilateral complex wells commonly referred in the literature as maximum reservoir contact (MRC) wells with mechanical components such as ICVs and ICDs.

In this paper, a new framework to model controlling the FCDs in complex well applications will be presented. The implementation is integrated into a complex well model. It can be easily used to model the dynamical control of devices. Simulation studies using both sector model and field model have been conducted. A systematic full-field operation is used for device control applications of smart wells. Successful application of field level controls in smart wells has the benefit of the improved overall GOSP performance.
Troubleshooting Gas Compression Systems Using Data Analysis
Authors: Al-Aiderous Abdullah
Published: December 2021

The objective of this paper is to showcase the successful and innovative troubleshooting data analysis techniques in one of the gas compression systems in upstream gas oil separation plants (GOSP-A). The gas compression system using gas compressors, dry gas seal systems and due point controls is used in almost all of upstream operation.

These proven data analysis techniques were used to tackle major and chronic issues associated with gas compression system operation that lead to excessive flaring, mechanical seal failures, solidification, hydrate formation and off-specification products. Dry Gas mechanical seals are an important key element in gas compression and its lifetime represents a concern to the operation personnel. Most gas compression systems have a mechanical seal lifetime of 2 years which in some cases limit production, increase the potential of unnecessary flaring and increase OPEX significantly. In addition, solidification due to constant liquid carry over result in a wide range of undesirable results such as blockages that constrain production rates and result in safety concerns.

In this paper, comprehensive data analysis of the potential root causes that aggravate undesired premature mechanical seal failure, material solidification, equipment damage and off-specification gas products will be discussed along with solutions to minimize expected impact. For example, improper product specification in some applications have been found to promote seal failures, corrosion, solidification and incur additional flaring which is both costly and environmentally undesirable. In addition, after extensive analysis improper operation practices during compressor startups, steady state operation and gas conditioning have been linked with premature compressor failures, product off spec and safety device failures.

The field trial proved the effectiveness of the proposed innovative troubleshooting data analysis techniques in reinstating the gas compression unit in GOSP-A to its recommended design conditions, eliminated compressors and mechanical seal failures and avoided the off-specification products at the lowest operating cost. This innovative technique was based on deep and extensive process data analysis, evaluating operating and design data, reviewing international standards, benchmarking against other facilities, process simulation using Hysys, and finally the actual field trial.
Machinery Fault Detection Through Ultrasound Technology
Authors: Alousif Ali , Alali Saad
Published: December 2021

Ultrasound is a versatile advanced technology that is utilized in the oil and gas industry for various mechanical and electrical applications such as bearing's faults detection, pump's cavitation, valve's leakage, steam traps, electrical faults, gearbox's issues, compressed air and gas leak's detection..etc. The technology allows the end-user to measure dynamic data using contact (Structure borne) and non-contact (air borne) sensors and converts the ultrasound waves to an audible range for humans to associate sounds with the measured signal. As a result, the sound of the machine can be heard and recorded as voice clip as well as time wave form, which in turn can be translated into frequency spectrum for analysis. The technology has recently evolved in the industry as an important condition monitoring tool, to increase the reliability of rotating equipment. Moreover, it used as a complementary tool to vibration analysis. As well, it can be used as a tool for troubleshooting and preventive maintenance inspection.

Background

Ultrasound is sound waves with frequencies that are higher than the upper audible limit of human hearing. The human hearing limit varies from person to another, and it is approximated to be around 20Hz to 20 kHz. This is in contrary to the ultrasound range, which is above 20,000 Hz, and hence, it is in audible to human. This range is used widely in various industrial processes, including: cleaning, cutting, forming, testing of materials, and welding. It is characterized by its directional waves, unlike normal sound waves that travel in all directions. This directional characteristic makes ultrasound useful for many applications. Furthermore, ultrasound technology is used in different fields: medical, automotive, etc. and recently in the oil and gas industry as non-destructive-testing tool (NDT).

The ultrasound technology in the oil and gas industry is used primarily in the following area's, for example

Leak detection. Steam traps inspection. Bearing condition monitoring. Bearing lubrication monitoring. Electrical Inspection. Valve condition monitoring. Pump cavitation. Gearbox issues.
Coupled Effect of Imbibition Capillary Pressure and Matrix-Fracture Transfer on Oil Recovery from Dual-Permeability Reservoirs
Authors: AlRamadhan Aymen , Cinar Yildiray , Hussain Arshad , BuKhamseen Nader
Published: December 2021

This paper presents a numerical study to examine how the interplay between the matrix imbibition capillary pressure (Pci) and matrix-fracture transfer affects oil recovery from naturally-fractured reservoirs under waterflooding. We use a dual-porosity, dual-permeability (DPDP) finite difference simulator to investigate the impact of uncertainties in Pci on the waterflood recovery behavior and matrix-fracture transfer. A comprehensive assessment of the factors that control the matrix-fracture transfer, namely Pci, gravity forces, shape factor and fracture-matrix permeabilities is presented. We examine how the use of Pci curves in reservoir simulation can affect the recovery assessment. We present two conceptual scenarios to demonstrate the impact of spontaneous and forced imbibition on the flood-front movement, waterflood recovery processes, and ultimate recovery in the DPDP reservoir systems of varying reservoir quality.

The results demonstrate that the inclusion of Pci in reservoir simulation delays the breakthrough time due to a higher displacement efficiency. The study reveals that the matrix-fracture transfer is mainly controlled by the fracture surface area, fracture permeability, shape factor, and the uncertainty in Pci. We underline a discrepancy among various shape factors proposed in the literature due to three main factors: (1) the variations in matrix-block geometries considered, (2) how the physics of imbibition forces that control the multiphase fluid transfer is captured, and (3) how the assumption of pseudo steady-state flow is addressed.
Human Factors in Domain Adaptation Within the Oil and Gas Industry
Authors: Ershaghi Iraj , Ershaghi Milad A. , Al-Ruwai Fatimah
Published: December 2021

A serious issue facing many oil and gas companies is the uneasiness among the traditional engineering talents to learn and adapt to the changes brought about by digital transformation. The transformation has been expected as the human being is limited in analyzing problems that are multidimensional and there are difficulties in doing analysis on a large scale. But many companies face human factor issues in preparing the traditional staff to realize the potential of adaptation of AI (Artificial Intelligence) based decision making.

As decision-making in oil and gas industry is growing in complexity, acceptance of digital based solutions remains low. One reason can be the lack of adequate interpretability. The data scientist and the end-users should be able to assure that the prediction is based on correct set of assumptions and conform to accepted domain expertise knowledge. A proper set of questions to the experts can include inquiries such as where the information comes from, why certain information is pertinent, what is the relationship of components and also would several experts agree on such an assignment. Among many, one of the main concerns is the trustworthiness of applying AI technologies

There are limitations of current continuing education approaches, and we suggest improvements that can help in such transformation. It takes an intersection of human judgment and the power of computer technology to make a step-change in accepting predictions by (ML) machine learning. A deep understanding of the problem, coupled with an awareness of the key data, is always the starting point. The best solution strategy in petroleum engineering adaptation of digital technologies requires effective participation of the domain experts in algorithmic-based preprocessing of data. Application of various digital solutions and technologies can then be tested to select the best solution strategies. For illustration purposes, we examine a few examples where digital technologies have significant potentials. Yet in all, domain expertise and data preprocessing are essential for quality control purposes
Enhancing Stratigraphic Framework Consistency Using Spectral Gamma-Ray Data
Authors: Nugraha Reza Satria , Tumbarinu Oliver Esteva
Published: December 2021

Stratigraphic correlation is crucial for reservoir characterization; therefore, it requires more advanced methods and techniques to reduce the stratigraphic correlation uncertainty, especially when variation in lateral facies is high. The studied formations from bottom to top consist of fluvial to marginal marine X Formation, shallow marine Y Formation, and fluvial distributary channels to estuarine Z Formation. Spectral gamma-ray logs give additional consistent information on lithological composition that can support identification of boundary between formations within the stratigraphic framework.

Wells with a full section of Y Formation, core, palynology, and spectral gamma-ray were selected as key wells. The top and base of the Y Formation were picked using conventional logs refined by a thorium/potassium (Th/K) ratio log and cross plot with core and palynology data as validations. The internal Y Formation markers were also picked with the aid of the Th/K cross plots. The top picking criteria from the key wells was implemented to the rest of the wells across the field with consistency.

The uniform low Th/K ratio log (<3.5) across the Y Formation indicates illite as the dominant clay type, confirmed by X-ray diffraction (XRD) data with an average of more than 80%. The character is consistent with the interpreted depositional environment. This character makes the Y Formation stand out from the overlying Z and the underlying X formations. The change from X to Y Formation is defined by the decrease of the Th/K ratio log, from high kaolinite content to illite dominated environment. Inversely, the top of the Y Formation (base of Z) is indicated by the increase of the Th/K ratio log moving from shallow marine Y Formation to the fluvial-influenced Z Formation. The Th/K cross plot indicates different clusters amongst the studied formations and the internal Y zonation. The X Formation is located in the high Th and low K area where kaolinite is predominant, related to fluvial environment. The case is similar for the Z Formation but with more influence of mixed-clay type. The Y Formation shows clear clustering along the mixed-clay and illite window. Internal Y zonation displays, from bottom to top, an increasing K value within the clusters. This method provides a semi-quantitative interpretation to define the studied formations boundaries and the Y Formation internal zonation.

This study has increased the consistency of the studied formations’ stratigraphic and structural framework. This consistency has, in turn, fine-tuned the structural framework and aided field development through better geosteering and lateral well placements. These results are a valuable starting point to refine and extend the work to other areas.
Seismic Wave Simulation in Fractured Media
Authors: Zhang Houzhu , Li Jiaxuan , Ali Abdulmohsen
Published: December 2021

Fractured reservoirs, including unconventional fields, are important in global energy supply, particularly for carbonate source rocks. Fractures can influence subsurface fluid flow and the stress state of a reservoir. The knowledge about the existence of fractures, their spatial distributions, and orientations can help us optimize well productivity and reservoir performance. Seismic detection of subsurface fractures provides important measurements to remotely image field-scale fractures. In developing such technology, forward modeling of the seismic response from fractures in the reservoir provides an important alternate tool for imaging subsurface fractures. In this paper, we implement a seismic modeling algorithm which can simulate 3D wave propagation in an arbitrary background media with imbedded fractures. During modeling, the fractures are added to the background medium by linear slip theory. Examples demonstrated the impacts of fractures on the wave propagation patterns for both PP and PS waves. We also investigate the amplitude versus offset (AVO) effects caused by fractures in a layer media and lay out potential applications of forward modeling in the inversion of fracture parameters and the estimation of fluid contents.
Integrated Cloud Computing Environment for Upstream Geoscience Workflows
Authors: Al-Habib Murtadha , Al-Ghamdi Yasser
Published: December 2021

Extensive computing resources are required to leverage todays advanced geoscience workflows that are used to explore and characterize giant petroleum resources. In these cases, high-performance workstations are often unable to adequately handle the scale of computing required. The workflows typically utilize complex and massive data sets, which require advanced computing resources to store, process, manage, and visualize various forms of the data throughout the various lifecycles. This work describes a large-scale geoscience end-to-end interpretation platform customized to run on a cluster-based remote visualization environment.

A team of computing infrastructure and geoscience workflow experts was established to collaborate on the deployment, which was broken down into separate phases. Initially, an evaluation and analysis phase was conducted to analyze computing requirements and assess potential solutions. A testing environment was then designed, implemented and benchmarked. The third phase used the test environment to determine the scale of infrastructure required for the production environment. Finally, the full-scale customized production environment was deployed for end users.

During testing phase, aspects such as connectivity, stability, interactivity, functionality, and performance were investigated using the largest available geoscience datasets. Multiple computing configurations were benchmarked until optimal performance was achieved, under applicable corporate information security guidelines.

It was observed that the customized production environment was able to execute workflows that were unable to run on local user workstations. For example, while conducting connectivity, stability and interactivity benchmarking, the test environment was operated for extended periods to ensure stability for workflows that require multiple days to run.

To estimate the scale of the required production environment, varying categories of users’ portfolio were determined based on data type, scale and workflow. Continuous monitoring of system resources and utilization enabled continuous improvements to the final solution.

The utilization of a fit-for-purpose, customized remote visualization solution may reduce or ultimately eliminate the need to deploy high-end workstations to all end users. Rather, a shared, scalable and reliable cluster-based solution can serve a much larger user community in a highly performant manner.
New Catalyst-Free Polycrystalline Diamond with Industry-Record Wear Resistance
Authors: Zhan Guodong David , Li Bodong , Moellendick Timothy Eric , He Duanwei , Xu Jianhui
Published: December 2021

PDC drill bits are the primary drilling tools for oil and gas in most of formations. In a PDC drill bit, PDC cutters are key cutting components to engage with these formations. However, there is often a big challenge for today's PDC drill bits when drilling very hard and abrasive formation. The main weakness in the PDC cutter is due to the unavoidable use of metallic catalyst which is used to bond the diamond grains in the PDC cutters. The thermal expansion of the metallic catalysts resulting from high frictional heat at the cutter/rock interface during drilling operation is higher than that of diamond grains, causing the thermal stress between the metallic catalyst and diamond grain which can break the PDC cutter. Therefore, development of catalyst-free PDC cutters would be a game-changing technology for drill bit by delivering significant increase in performance, durability, and drilling economics. In this study, an innovative ultra-high pressure and ultra-high temperature technology was developed with ultra-high pressures up to 35 GPa, much higher than current PDC cutter technology. We report a new type of catalyst-free PDC cutting material, synthesized under one of conditions using ultra-high pressure of 16 GPa. The new material breaks all single-crystal-diamond indenters in Vickers hardness testing which sets a new world record as the hardest diamond material as of today. Also, the material shows the highest thermal stability in the family of diamonds in air at 1,200°C, which is about 600 °C higher than current PDC cutters. As a consequence of these superior properties, this new material exhibited industry-recorded wear resistance, which is four times of that of current PDC cutters. All of these achievements demonstrated a breakthrough in PDC cutter technology development and presented a feasibility for the goal of "One-Run-To-TD" game-changing drilling technology.
First-Break Picking Classification Models Using Recurrent Neural Network
Authors: Ayub Mohammed , Kaka SanLinn
Published: December 2021

Manual first-break picking from a large volume of seismic data is extremely tedious and costly. Deployment of machine learning models makes the process fast and cost effective. However, these machine learning models require high representative and effective features for accurate automatic picking. Therefore, First- Break (FB) picking classification model that uses effective minimum number of features and promises performance efficiency is proposed. The variants of Recurrent Neural Networks (RNNs) such as Long ShortTerm Memory (LSTM) and Gated Recurrent Unit (GRU) can retain contextual information from long previous time steps. We deploy this advantage for FB picking as seismic traces are amplitude values of vibration along the time-axis. We use behavioral fluctuation of amplitude as input features for LSTM and GRU. The models are trained on noisy data and tested for generalization on original traces not seen during the training and validation process. In order to analyze the real-time suitability, the performance is benchmarked using accuracy, F1-measure and three other established metrics. We have trained two RNN models and two deep Neural Network models for FB classification using only amplitude values as features. Both LSTM and GRU have the accuracy and F1-measure with a score of 94.20%. With the same features, Convolutional Neural Network (CNN) has an accuracy of 93.58% and F1-score of 93.63%. Again, Deep Neural Network (DNN) model has scores of 92.83% and 92.59% as accuracy and F1-measure, respectively. From the pexperiment results, we see significant superior performance of LSTM and GRU to CNN and DNN when used the same features. For robustness of LSTM and GRU models, the performance is compared with DNN model that is trained using nine features derived from seismic traces and observed that the performance superiority of RNN models. Therefore, it is safe to conclude that RNN models (LSTM and GRU) are capable of classifying the FB events efficiently even by using a minimum number of features that are not computationally expensive. The novelty of our work is the capability of automatic FB classification with the RNN models that incorporate contextual behavioral information without the need for sophisticated feature extraction or engineering techniques that in turn can help in reducing the cost and fostering classification model robust and faster.
Uncertainty Quantification and Optimization of Deep Learning for Fracture Recognition
Authors: Santoso Ryan , He Xupeng , Alsinan Marwa , Kwak Hyung , Hoteit Hussein
Published: December 2021

Automatic fracture recognition from borehole images or outcrops is applicable for the construction of fractured reservoir models. Deep learning for fracture recognition is subject to uncertainty due to sparse and imbalanced training set, and random initialization. We present a new workflow to optimize a deep learning model under uncertainty using U-Net. We consider both epistemic and aleatoric uncertainty of the model. We propose a U-Net architecture by inserting dropout layer after every "weighting" layer. We vary the dropout probability to investigate its impact on the uncertainty response. We build the training set and assign uniform distribution for each training parameter, such as the number of epochs, batch size, and learning rate. We then perform uncertainty quantification by running the model multiple times for each realization, where we capture the aleatoric response. In this approach, which is based on Monte Carlo Dropout, the variance map and F1-scores are utilized to evaluate the need to craft additional augmentations or stop the process. This work demonstrates the existence of uncertainty within the deep learning caused by sparse and imbalanced training sets. This issue leads to unstable predictions. The overall responses are accommodated in the form of aleatoric uncertainty. Our workflow utilizes the uncertainty response (variance map) as a measure to craft additional augmentations in the training set. High variance in certain features denotes the need to add new augmented images containing the features, either through affine transformation (rotation, translation, and scaling) or utilizing similar images. The augmentation improves the accuracy of the prediction, reduces the variance prediction, and stabilizes the output. Architecture, number of epochs, batch size, and learning rate are optimized under a fixed-uncertain training set. We perform the optimization by searching the global maximum of accuracy after running multiple realizations. Besides the quality of the training set, the learning rate is the heavy-hitter in the optimization process. The selected learning rate controls the diffusion of information in the model. Under the imbalanced condition, fast learning rates cause the model to miss the main features. The other challenge in fracture recognition on a real outcrop is to optimally pick the parental images to generate the initial training set. We suggest picking images from multiple sides of the outcrop, which shows significant variations of the features. This technique is needed to avoid long iteration within the workflow. We introduce a new approach to address the uncertainties associated with the training process and with the physical problem. The proposed approach is general in concept and can be applied to various deep-learning problems in geoscience.