bluuebunny/crossref_metadata_2025 huggingface dataset:
Smart Real Time Data Transfer Surveillance with Edge Computing and Centralized Remote Monitoring System
Authors: Nugraha Budhi , Nair Ragesh , Muhammad Kashif
Published: January 2020

High-quality and completeness of real time drilling data have become critical factors to capitalize the improvement of data analysis for decision making. This paper proposes a combined architecture that supports edge computing technology with high frequency real time data to produce smart alerts as well as for the remote monitoring of real time data status for large number of rigs in a centralized location to enhance the surveillance of real time drilling data transfers.

Unlike the traditional real time data surveillance method that monitors the data in the WITSML town server, this innovative technique analyzes the real time data quality right on the edge device. It offers smart computation to produce rule-based intelligent alarms for effective decision-making. This method delivers flexibility and effectiveness in WITSML real time data monitoring as it is fully independent from direct access to the WITSML Store. The biggest advantage of this approach is that it handles the remote surveillance of numerous rigs and gives absolute control over data aggregation.

Managing sensitive drilling data has started to play a key role in cost optimization and critical decision- making, especially when running an active mission critical operation. The new approach of edge computing to analyze the WITSML data from its activity file at the rig site is a breakthrough in the real time data monitoring concept. A rule-based smart alarm system to check the statistics of real time data on the edge increases the confidence of the decision-making process as any data transfer issue can be detected faster. Further analysis and active monitoring of multiple rigs data transmission can be done by a centralized control panel, accessed from the real time operations center in town. Having a system with a precise method to actively monitor the status of real time data transfers on multiple rig sites and smartly generate alerts, assures end users to manage their well efficiently.

Emphasizing the requirement to ensure the nature, quality and latency of real time information at rig site will reduce the lost and non-productive time overall.
Extending Salinity Operating Range and Water Detection Lower Limit of Multiphase Flowmeters
Authors: Fiore Massimiliano , Xie Cheng-Gang , Jolivet Guillaume
Published: January 2020

With 20 years of operations, multiphase flowmeters (MPFMs) using a combination of venturi and multi-energy gamma-ray absorption have gained acceptance in the oil and gas industry by demonstrating reliable measurement and operational robustness. Nonetheless, some challenging operating scenarios are encountered such as significant water salinity changes in multiphase and wet-gas flows and rapid detection of small water quantities during production.

Although MPFMs are not affected by small water salinity changes, inaccurate flow rates can be calculated when water salinity significantly departs from the initial properties. To maintain measurement metrology as per specifications, the water-point should be manually modified to track variations; however, this operation is time consuming and may incur nonproductive time. The salinity change measured by a newly developed sensor can be provided seamlessly to MPFMs to automatically update the water point calibration, thus ensuring good flow rate measurement performance over a wider water salinity range. Extensive validations at several flow loop facilities and field tests, including frac flowback operations, demonstrate the robustness of the method in terms of hardware reliability and the delivery of high-quality data when used with MPFMs with digital connectivity, which opens the way to unmanned production monitoring operations. Interpreting multiphase mixture's dielectric constant and electrical conductivity simultaneously measured by the new microwave water salinity sensor at a high data acquisition rate shows that water salinity can be tracked online in the presence of oil, gas, and sand, and small water quantities down to 50 ppm (part-per-million) water volume fraction (WVF), from the typical ~1000-ppm WVF for the MPFM, can be detected rapidly in multiphase and wet-gas flows.

This paper describes the working principles of the new sensor that enable additional capabilities to maintain accurate water-liquid ratio (WLR) measurement in situations where significant water salinity changes occur and to provide a rapid, much lower WVF detection level. Successful cases demonstrating an accurate real-time salinity monitoring in flow loop tests and in field operations are also presented.
Compositional Tracking: Predicting Wellstream Compositions in Tight Unconventionals
Authors: Carlsen Mathias Lia , Dahouk Mohamad Majzoub , Mydland Stian , Whitson Curtis Hays
Published: January 2020

Wellstream compositions are needed for a range of critical engineering applications but it is impractical and too expensive to acquire these compositions frequently (e.g. daily) for every well in an unconventional basin. Detailed PVT studies that provide wellstream compositions can easily cost 25,000–50,000 USD and are typically performed on less than 1% of the wells in a "shale" basin. This paper provides methods to predict wellstream compositions daily in wells without detailed PVT analysis by using readily available production and well test (fluid sampling) data.

The three different methods presented all leverage a field-wide equation of state (EOS) model together with a varying degree of available production and well test data. The EOS model used should honor all the available fluid data in the relevant field, region, or formation, and should be developed based on as many detailed PVT studies as possible. The difference between the three different methods is the amount and type of data typically available, ranging from production data only to extensive compositional well test data.

This paper shows that accurate wellstream compositions can be predicted by using a field-wide EOS model together with production and well test data. The accuracy of the different methods depends on (1) the amount of available measured data, (2) the quality of the available measured data and (3) how well the EOS model captures the phase characteristics of the relevant fluid. The proposed methods provide a tool to consistently and accurately calculate daily wellstream compositions for all wells in a cost-efficient manner.

EOS models enable convenient and flexible calculations for describing phase behavior of petroleum fluids. However, to use an EOS model, three input parameters are needed: temperature, pressure and composition. In unconventionals, temperature and pressure are almost always available, but wellstream composition is not. The purpose of this work is to make wellstream compositions readily available for unconventional fields with thousands of wells, and thereby unlock the potential for EOS model utilization.
3D Confocal Imaging Methodology Optimized for Pore Space Characterization of Microporous Carbonate Reservoirs
Authors: Hassan A. , Chandra V. , Yutkin M. P. , Patzek T. W.
Published: January 2020

Microporous carbonates host a significant portion of the remaining oil-in-place in the giant carbonate reservoirs of the Middle East. Carbonates host wide range of pore sizes, however the key element influencing hydrocarbon flow is pore interconnectivity. We evaluate the use of confocal microscopy to image fluorescent epoxy pore casts of microporous carbonates. The acquired high-resolution 3D confocal images are used to gain invaluable insights on the interconnectivity between macroporosity and microporosity. We analyzed the sensitivity of quality of epoxy pore cast images to: fluorochrome selection, objective lens, and imaging medium by imaging standard fluorescent spherical beads. Guided by the sensitivity results, we acquired 3D images of the multi-modal pore space in an Indiana limestone sample with lateral- and axial-resolution of 0.36 µm and 2 µm, respectively. And we were able to identify the multi-scale pore types in the studied carbonate sample and highlight their interconnectivity.
Hybrid Artificial Intelligence Techniques for Automatic Simulation Models Matching with Field Data and Constrained Production Optimization
Authors: Giuliani Marco , Cadei Luca , Montini Marco , Bianco Amalia , Grimaccia Francesco , Mussetta Marco , Niccolai Alessandro
Published: January 2020

Asset optimization has recently become a crucial issue in Oil&Gas industry, considering oil price conjuncture and an increased awareness on environmental aspects. In this paper, an Artificial Intelligence technique is presented, which is able to manage big datasets to automatically match the production models and propose an operative solution that maximizes the production. The tool is based on a hybrid in-house developed AI technique, integrating deep neural networks, biogenetical algorithms, commercial simulators and real-time data.

The workflow starts with the modeling of the production system through physics based commercial simulators. A sensitivity analysis identifies the critical variables, which are then randomly varied with a Sobol distribution, exploring the entire solution domain. With these data, a proxy model to the commercial software is generated: it consists in an artificial neural network able to replicate the field behavior. Finally, the AI tool fed by real-time data is used firstly to match the field behavior and, successively, to maximize the field production.

The tool has been developed considering an offshore oil field. The typical uncertain parameters used in the matching phase are fluid characteristics, in particular densities and compositions, but also some pipeline physical characteristics. The typical optimization parameters could range from choke opening and gas lift to plant operating temperatures. The matching process is performed coupling the proxy model with a differential evolution algorithm whose fitness function is an error function, to be minimized, that represents the distance between the actual field production parameters and the modeled ones. The algorithm finds the solution by varying the uncertain parameters and keeping fixed the known optimization ones. Once the model matches with the production data, the set of uncertain parameters is defined and fixed and the process of optimization can start, by changing the optimization parameters. This is achieved with a second differential evolution algorithm working with the same proxy model. The fitness function in this phase is the oil production, which must be maximized, having as constraint the maximum gas flow rate treatable by the plant. This tool has proven to be able to solve the problem in less than a minute.

In this paper, the substantial advantages of substituting the physical model with a proxy one have been shown. With the novel approach, the computational time has been reduced by three orders of magnitude with respect to a classic method. This is a breakthrough because it allows the matching and optimization procedures to be done on a daily basis, which results in having always tuned model and a powerful tool to monitor online the field production.
Significance of Aeolian Deposits Resultant Drift Direction, Implications for Reservoir Prediction, Utilizing Subsurface and Analogue Data
Authors: Al-Masrahy Mohammed
Published: January 2020

Petroleum is common and abundant in wind-laid rocks in many places in the world, and aeolian deposits typically form good reservoirs or pathways for hydrocarbon migration due to their high initial porosity and permeability, and the generally great lateral continuity of preserved elements. Subsequently, heterogeneity in such aeolian successions, e.g., arising due to the juxtaposition of dune and interdune elements, can be problematic; therefore, there is a need to develop quantitative models for predicting the arrangement of such elements in subsurface successions.

The Artinskian-Kungurian Upper Unayzah Formation displays widespread evidence of deposition in a continental setting under the influence of an arid to semi-arid climatic regime, commonly in a wind dominated, aeolian setting. In the study areas, a number of distinctive aeolian depositional facies are recognized from subsurface cores and image logs, including: aeolian sand dune, interdune/damp sandflat, sand sheets/dry sandflat and ephemeral playa lake palaeo-environments. The facies occur with a high degree of vertical and areal repeatability throughout the studied successions. The study further investigated distribution of azimuthal variability of the cross-bedded sandstone's aeolian sand dune facies in the Lower Permian, utilizing microresistivity borehole image logs. A palaeo-wind data study involved the analysis of image log data from several wells. To investigate the palaeo-wind directions from the measured subsurface azimuthal data, a hierarchical approach was employed as follows: (i) azimuth data recorded for each identifiable bed, (ii) averaged over each bedset within each well, (iii) averaged over each well, (iv) averaged over each area, and (v) averaged over the four studied areas.

This rigorous approach resulted in the identification of a resultant drift direction toward east-northeast (relative to the present day), with a range that varied between 40° and 100° with a mean direction of 72 azimuth degrees. The final stage of this study was to analyze wind data obtained from modern analogues to further understand and evaluate the sand drift directions and potentials. Identification of the dominant palaeo-wind direction served as the basis for developing a model for palaeo-environmental spatial and temporal variations in dune distribution and planform morphological type throughout the studied areas. This model then formed a principal tool used for field development plans by enabling prediction and mapping of porosity and permeability distributions.
Automated Hole Cleaning Monitoring: A Modern Holistic Approach for NPT Reduction
Authors: Forshaw Matthew , Becker Gerald , Jena Sanjib , Linke Christian , Hummes Olof
Published: January 2020

NPT is thought to account for upwards of 30% of the upstream costs in O&G production, half of which can be attributed to downhole drilling problems, within which hole cleaning is arguably the most prominent offender, accountable for tens of millions of dollars in lost time costs.

The objective of this paper is to outline a modern, holistic, automated approach to hole cleaning that closely replicates human experience within a suite of software applications, allowing the early identification, and therefore early mitigation and remediation of this significant drilling issue.

The principles of hole cleaning as a drilling process are generally considered to be well understood, and both the accurate diagnosis during the drilling operation and the means to either mitigate or remediate the issue exist. While significant technological advances regarding detection of hole cleaning dysfunctions have been made in various individual approaches, the best certainty in identification has still been achieved where a human aggregates the individual results of these. The paper explores how such a holistic approach can be automated through an innovative digital solution.

This paper describes the individual advances made in individual system components, including real-time engineering modelling, surface and downhole direct measurements with intelligent agents, and offset experience. We then look at how these various components can be aggregated by an automated higher-level process to produce a result akin to that of an experienced human at the wellsite. Case studies from initial system deployments demonstrate both the validity and the financial impact of such an approach. Several of the ‘component’ services are described in more detail, including topics within torque and drag and pressure regime modelling are advances on current practice. Additionally, the concept of exploring aggregated multiple point-source findings in an application, just as a human would, is a framework that has significant potential when automating the detection of other drilling dysfunctions.

These concepts are expected to greatly reduce drilling risks and achieve predictable drilling performance at scale, in various drilling environments.
How Feasible Moving Target Defense is Within ICS Environment
Authors: Almusaher Hadi , Alam Gulzar
Published: January 2020

Industrial Control Systems (ICS) are designed to work for long periods of time with a very deterministic behavior as availability and reliability are the most important requirements mandated in operating critical infrastructures. Embracing cyber security defenses in such environments is of utmost importance as compromising these systems may lead to significant impacts to the facilities operated by such systems. Here, the order is control, availability, integrity and confidentiality as opposed to CIA model adopted in equivalent IT networks. This paper attepts to evaluate the feasibility and effectiveness of moving target defense as security protection within ICS environment considering packet latency as the main metric. Among the available MTD mechanisms, this paper selects to simulate the IP randomization capitalizing on the use of Software Defined Networks (SDN). It encourages applying MTD within ICS environment provided that packet size is maintained around 1024 bytes which introduces a delay under 50 mSec, an acceptable latency to large ICS installations around the world.
Estimation of the Total Organic Carbon Using Functional Neural Networks and Support Vector Machine
Authors: Mahmoud Ahmed Abdulhamid , Elkatatny Salaheldin , Ali Abdulwahab , Abdulraheem Abdulazeez , Abouelresh Mohamed
Published: January 2020

Total organic carbon (TOC) is an important factor for the characterization of unconventional shale resources; which is currently evaluated by either conducting extensive laboratory work, using empirical correlations developed based on linear regression analysis, or applying artificial intelligence (AI) techniques. The AI models approved their efficiency for TOC estimation compared to the use or empirical correlation and they have the advantage of providing a continuous TOC profile compared to the laboratory-based evaluation.

This study is aimed to evaluate the predictability of the TOC using two AI models namely functional neural networks (FNN) and support vector machine (SVM). The AI models were trained to estimate the TOC based on well log data of gamma ray, deep resistivity, sonic transit time, and bulk formation density, more than 500 datasets of the well logs and their corresponding core-derived TOC collected from Barnett shale were used to train and optimize the AI models. The predictability of the optimized AI models was then tested on other data from Barnett shale and validated on unseen data from Devonian shale. The ability of the optimized AI models to estimation the TOC for Devonian shale was compared with Wang's density-based correlation (WDC) which was developed recently to estimate the TOC for Devonian formation.

The results showed that the AI models predicted the TOC with high accuracy, and they overperformed WDC in estimating the TOC for Devonian formation. For the validation data, FNN model overperformed SVM in estimating TOC with average absolute percentage error (AAPE) of 12.0% and correlation coefficient (R) of 0.88, while SVM model predicted the TOC with AAPE and R of 14.5% and 0.86, respectively, and WDC estimated the TOC with high AAPE of 34.6% and low R of 0.61.
Integrated Hydraulics Modeling for Managed Pressure Drilling
Authors: Rostami Sayamik A. , Brana Jose , Koithan Thomas
Published: January 2020

Since the latest downturn of the Oil and Gas (O&G) sector, productivity growth has become the priority, which led to the adoption of new key technologies for better performance and lower cost. As part of the 4th Industrial Revolution, these technologies are taking the O&G by storm, forcing hardware manufactures to innovate quickly and create devices with more capabilities. What was impossible a few short years ago is reality today.

Managed pressure drilling (MPD) systems are taking advantage of this development by implementing advanced data analytics and hydraulic modeling in programable automation controllers. This approach has several benefits, reliability and full real-time deterministic operation being the major ones. Previously, controlling bottomhole pressure (BHP), the required modeling and analysis were executed in a networked computer at a different physical location. Now it is executed locally in a fully deterministic control system. This includes data acquisition (DAQ), signal conditioning, well bore modeling, analytics and data visualization.

This approach mitigates the risk of connectivity issues and makes the system more robust in case of network failure. Furthermore, it simplifies the deployment of the system, as all critical functions are at a single physical location. Another major benefit is an increased resolution of the model in areas of specific interest. For example, a zoom in on the openhole section and a lower resolution in the cased hole section. The flexibility of this approach manifests itself on the deployment side. This system can be installed in several different scenarios, for example as a full system with controls and manifolds, or as a control system only, and integrated into existing rig MPD manifolds and drilling controls. This level of integration creates robust and flexible systems while reducing downtime during operations.

This new approach includes the integration of cyber secure communications, an upcoming standard in the O&G industry. The system communicates via the open platform communications unified architecture (OPC UA) protocol – recommended by the Cyber Security community.

This new platform integrates advanced data analytics and hydraulic modeling in real-time, coupled with cyber security features via OPC UA protocol directed to automation controllers for managed pressure drilling applications.
Reliability Analysis Methodology for Oil and Gas Assets: Case Study of Subsea Control Module Operating in Deep Water Basin at Brazilian Pre-Salt
Authors: Signorini Geraldo , Rigoni Emerson , Rodrigues Marcelo
Published: January 2020

As deeper the oil and gas production goes more important the remote monitoring and control of the subsea assets become, and more reliable they need to be. This work aims to perform a quantitative and qualitative reliability study in a key and complex asset which is part of the subsea production system, the Subsea Control Module (SCM). The benchmark used to evaluate the performance of the assets is the Offshore and Onshore Reliability Data (OREDA) which is a worldwide database from failure records from all segments of the oil industry and different operating companies.

The methodology used in this paper is applicable to any equipment in the oil and gas industry, which was based on statistical analysis and functional study through RAM analysis using block diagram. For this case study a field dataset from 106 SCMs were gathered, analyzed and compared with the OREDA 2015 benchmark.

The reliability distribution for this specific dataset demonstrates an infant failure pattern which is aligned with benchmark in the industry for complex hydraulic and electronic systems, even though for 5 years operating time systems achieves a reliability of 86,83% outperforming the average benchmark from OREDA. The methodology allows a better understanding of how far from a world class performance the asset of interest is and which components are bringing your reliability down. One of the limitations of this study is the few data points available.

The production in deep water and ultra-deep-water fields are increasing every year due to the decrease of production in mature fields. This work contributes with the current literature with field data from deep water operations of Subsea Control Module (SCM), which is a very relevant dataset due to the limited quantity of SCMs in those types of fields. Additionally, the methodology presented here is simple and can be applied using free software.
Real-Time Well Log Prediction From Drilling Data Using Deep Learning
Authors: Kanfar Rayan , Shaikh Obai , Yousefzadeh Mehrdad , Mukerji Tapan
Published: January 2020

The objective is to study the feasibility of predicting subsurface rock properties in wells from real-time drilling data. Geophysical logs, namely, density, porosity and sonic logs are of paramount importance for subsurface resource estimation and exploitation. These wireline petro-physical measurements are selectively deployed as they are expensive to acquire; meanwhile, drilling information is recorded in every drilled well. Hence a predictive tool for wireline log prediction from drilling data can help management make decisions about data acquisition, especially for delineation and production wells. This problem is non-linear with strong ineractions between drilling parameters; hence the potential for deep learning to address this problem is explored. We present a workflow for data augmentation and feature engineering using Distance-based Global Sensitivity Analysis. We propose an Inception-based Convolutional Neural Network combined with a Temporal Convolutional Network as the deep learning model. The model is designed to learn both low and high frequency content of the data. 12 wells from the Equinor dataset for the Volve field in the North Sea are used for learning. The model predictions not only capture trends but are also physically consistent across density, porosity, and sonic logs. On the test data, the mean square error reaches a low value of 0.04 but the correlation coefficient plateaus around 0.6. The model is able however to differentiate between different types of rocks such as cemented sandstone, unconsolidated sands, and shale.
Deep Learning for Monitoring Well Testing Burner Efficiency
Authors: Mansour Darine , Arabi Hakim , Yu Yingwei , Trifol Hugues
Published: January 2020

During well testing operations, an incorrect burner combustion may pose a risk of adverse impact on the environment or people. The combustion efficiency is assessed by personnel who observe the flame. This practice lacks consistency and may pose challenges, from environmental and safety considerations to data quantity and quality. An automatic alarm triggered by suboptimal combustion is needed. In this paper, we propose a solution that uses a deep neural network that learns from flame videos to define the quality of the combustion.

Flame features that help determine the combustion quality are the flame color, the color of the smoke around the flame and the flame shape and pattern, among many others. To extract the features relevant to the problem and qualify the combustion, we performed supervised learning using a convolutional neural network. We trained the network using videos of flames from burner combustion; the network extracts relevant features and classifies the flame in video images as acceptable or undesirable.

We labeled a set of flame videos obtained from well testing operations. The videos include various flame conditions, in day and night scenarios and acceptable and undesirable combustion contexts. We trained the convolutional neural network using the labeled flame videos.

The network prediction accuracy is 100% on the training set and 96.8% on the test set. The misclassified images are in the transition periods between acceptable and undesirable combustion events.

We demonstrated the potential of a deep learning solution using a convolutional neural network for classifying the combustion quality of burner images as acceptable or undesirable. The results are promising, and they show that this solution is a good candidate for real-time burner efficiency monitoring and automatic alarm triggering and optimization.
Evaluating Operational Capabilities of High-Speed Gas Handling for Downhole Oil Production
Authors: Ejim Chidirim
Published: January 2020

High-speed electric submersible pumps (ESPs) for downhole oil production typically operate with rotational speeds in excess of twice that of conventional pumps. These pumps need gas handlers to ensure satisfactory operation at higher gas volume fractions (GVFs). This study presents an evaluation of the operational capabilities of high-speed gas handling. Knowledge of these capabilities aids production engineers and field operators to make more informed decisions for efficient downhole oil production operations.

The evaluation was performed for high-speed gas handling for downhole pumps with 2.17, 3.19, 338, 400 and 538 series housing sizes. These pump size architectures were for typical casing/liner diameters varying from 4.5" to 7.0". In the evaluation, the inlet pressure was fixed, whereas the GVF and rotational speeds were varied. The GVF range was from 0 to 0.75, and the rotational speed varied from 3500 to 12000 revolutions per minute (RPM). The total volume flow rates varied from 4500 barrels of fluid per day (BOFPD) to upward in excess of 10000 BOFPD.

The results show that for a given inlet pressure increasing the rotational speed of a gas handling device increases its capability to process and pump the downhole fluids. There is also a tendency for higher gas handling capability when the rotational speed of the device is fixed and the dimensional size of the downhole equipment is varied. The observation will tend to suggest that for a given GVF, the gas handler operating at a higher rotational speed would be more tolerant to gas than the unit operating at a lower rotational speed. There is a tendency for the operational capability of the gas handler to be limited by the rotor dynamics and mechanical component, such as the shaft. This limitation will tend to depend on the size of the gas handling unit. These observations suggest an upper operation limit of the high-speed gas handling system can be attributed to a limitation of some of the specific system components.

This study highlights the importance of understanding capabilities of high-speed gas handling systems, which are needed for high gas content wells for downhole oilfield production operations. Knowing the parameters that limit high-speed gas handling is beneficial for deciding which device is most appropriate to use in a particular well. Such knowledge is vital for the operator to make strategic decisions to efficiently produce hydrocarbons using the field asset, and maximize the economic bottomline for the operator.
Optimization of Smart Well Placement in Waterfloods Under Geological Uncertainty in Intelligent Fields
Authors: Temizel Cenk , Canbaz Celal Hakan , Alsaheib Hasanain , Monfared Hashem
Published: January 2020

Latest technological developments and applications made optimal control methods usage in optimal well placement in intelligent fields practical and beneficial to increase the production. Effective usage of these methods strongly depends on the detailed evaluation of the economic view and performance in reservoirs that have high uncertainty, particularly. There are several methods of optimization of well placement ranging from classical reservoir engineering to derivative-free and hybrid methods.

TNO's Olympus model used globally as a benchmark model in ISAAP-2 Challenge in used. Geological modeling software is coupled with the commercial full-physics reservoir simulator as well as the optimization software in order to produce different geological realizations to represent the geological uncertainty and run the simulation model with differing inputs of optimization and uncertainty in a loop. Results are outlined in detail in a comparative way including comparison to the previous study to illustrate the challenges and benefits of smart wells and optimization of placement of them in intelligent fields.

Results indicate that classical reservoir engineering principles still prove useful in the beginning of the optimization process. Then, derivative-free and hybrid methods introduce significant improvement on economics. There are certain challenges in CPU requirements however the state-of-the-art facilities provided significant reduction in runtimes along with the help of the hybrid methods where proxies are built and used for faster runtimes. Despite higher initial capital expenses, smart wells provide significant advantages in recovery and economics compared to that of the conventional wells where these is less control on the production/injection at the layer level. Literature lacks a comprehensive study that takes into account the optimization of well placement in smart fields focusing on smart wells and the all major available methods for optimization. This study closes that gap providing a strong reference building on top of the previous study extending it to intelligent fields which are becoming very common and useful in oil and gas industry in conventional and unconventional applications.
Research and Application of Wellbore Fixed-Point Acidification Unblocking Technology for High Temperature and High Pressure Gas Wells
Authors: Wu Hongjun , Liu Ju , Wang Xi , Sun Tao , Kong Change , Liu Jiquan , Zhang Hui
Published: January 2020

In recent years, the high temperature and high pressure gas wells production in Kuqa mountain front zone were severely restricted due to the blockage of the wellbore, which affected the efficient development of gas field group of the Kuqa mountain front. The Dina and Keshen blocks were the main gas fields and the wellbore blockage problem was more severe. The problem in the Keshen gas field was mainly concentrated in the Keshen2 block. More than 50% gas wells had that problem, which leaded millions of square meters of production loss compared with the full production. As for Dina gas field, the problem was severer and more than 80% wells had wellbore blockage problems, which resulted in reducing the gas production by more than half, and even shutting down the production.

For the severely blocked wells, the coiled tubing tool was used to unblock and the wellbore blockage sample was finely collected. It could be drawn that the wellbore blockage distribution was partly blocked and blockage position was likely to occur at the reduced diameter throttling tool or the screen pipe. The amount of blockage was less than 30L, and the main component of the blockage was measured as scale.

Based on the understanding of the blockage, a "9%HCl+1%HF" acid unblocking liquid system was developed. The dissolution rate of the liquid system to the scale was greater than 90%, and the dissolution rate of the formation sand sample was greater than 34%. For blocked wells with flow passages available in the wellbore, the wellbore fixed acidification was used and the unblocking process was that under the condition of controlling the pump pressure not higher than the maximum allowable pressure of the wellhead, acid unblocking solution was injected at low-displacement to dissolve the wellbore blockage and to restore gas well productivity. Compared with the coiled tubing sand washing, wellbore fixed acidification had the effect of better performance, simple process and low time consumption.

A total of 14 wells had a success rate of 100%. After fixed-point wellbore acidification, the single-well oil pressure increases by 30MPa. The amount of gas production was increased by 207×104 m3/d, and the open-flow capacity of 71% wells was equal or exceeded to the capacity at the initial production stage.
Improvements in Waste Heat Recovery Units Used in Oil and Gas Facilities
Authors: Siem Nelson
Published: January 2020

The aim of this paper is to describe significant potential risks that were identified in a typical waste-heat recovery unit (WHRU) used in a natural gas processing unit and to recommend some improvements and best practices in order to minimize those potential hazards that might lead to operational incidents, particularly during the commissioning phase. A case study of a WHRU operating in a gas processing is taken as example and where a heating coil circulating a thermal fluid recovers the waste heat from the exhaust gas of a gas turbine that serves for power generation in the processing of raw natural gas. The potential risks identified are the possibility of coking formation in the circulating thermal fluid, the surge of high backpressure in the exhaust gas from the turbine and, finally, the possibility of corrosion in the dampers and heating coil.

The paper provides valuable guidelines and best practices to prevent operational problems during the startup and operation of the WHRU from gas turbines. The recommendations are intended to minimize the risks by improving the sealing airflow to the main damper with the proper dimension of the piping network and by avoiding stagnant flow of the thermal fluid. Additional recommendations include monitoring constantly the exhaust gas backpressure to prevent the pressure surge, as well as the installation of mechanical devices that can prevent the full closure of the two dampers simultaneously. Finally, the selection of the recommended construction materials can minimize the potential risks of corrosion.
First Successful Installation of Progressing Cavity Pump System in an Oil Well at the Kingdom of Saudi Arabia
Authors: Zein El Din Shoukry Ahmed , Soltys Tim Wayne , Bettenson John , Ariza Gustavo
Published: January 2020

This paper explains the benefits of using Progressing Cavity Pump (PCP) as a reliable Artificial Lift System (ALS) to be deployed during short term drill stem testing (DST) operations in mid to shallow heavy oil wells in Kingdom of Saudi Arabia.

A screening of the available ALS's was performed based on the selected candidate well's conditions. Aspects such as flow rate, API gravity, equipment installation simplicity and equipment capability for redeployment were evaluated in order to pick the most cost-effective option for this application.

During the planning stage in the development of a new oilfield, the selection of the artificial lift system (ALS) to be install is one of the more critical decisions. Aspects such as fluid flow rate, API gravity of the fluid to be produced, equipment installation simplicity and its capability for redeployment must be evaluated in order to pick the most cost-effective option for the application. The proper selection of the ALS will rely on the parameters previously mentioned as well as others. Below is a table that contains some parameters to be considered when evaluating ALS options

ALS application screening values.
Quantifying Separator Oil Shrinkage
Authors: Carlsen Mathias Lia , Whitson Curtis Hays
Published: January 2020

In tight unconventionals, oil and gas rates are often measured daily at separator conditions. Consequently, it is necessary to reliably convert these rates to volumes at standard conditions in cases where direct stock-tank measurements are not available. Due to changes in producing wellstream compositions and separator conditions, the separator-oil shrinkage factor (SF) can change significantly over time. This paper presents a rigorous and consistent method to convert daily separator rates into stock-tank volumes. Recommendations for developing field-specific shrinkage correlations using field test data are also proposed.

To study how separator-oil shrinkage changes through time for tight unconventional wells, four reservoir fluids have been studied using a compositional reservoir simulator: (1) black oil, (2) volatile oil, (3) near-critical oil, and (4) gas condensate. All the simulations were conducted using a common equation of state (EOS) model, such that the only difference between each of the different runs were the in-situ reservoir fluid composition. The daily simulated wellstreams were thereafter run through a set of i) constant separator conditions and ii) changing separator conditions to analyse how daily separator oil shrinkage factors (SF) change with time.

Separator oil shrinkage factor, and the associated liberated gas, change with time due to changes in producing wellstream compositions and separator conditions (zi, psep, Tsep). The variations of shrinkage factors through time are more severe for near-critical volatile oils and gas condensates, than for black- and volatile oils. This paper shows that not accounting for these changes can lead to errors in converted stock-tank oil rates of more than 20%.

To convert separator rates to stock tank volumes, the common practice is to apply one constant shrinkage factor for a well and/or region. This paper shows that this practice can lead to significant errors, especially when producing from near-critical volatile oil and gas condensate fluid systems. This paper provides a novel approach for rigorous and consistent translation of daily separator-oil rate measurements into stock-tank rates, where we believe this procedure is seldom applied and can be important to allocation, ownership royalty determination, and consistency related to forecasting.
Cement-Conveyed Technology Improves Zonal Isolation and Stimulation Efficiency
Authors: Perroni Dominic , Medvedev Anatoly , Kolchanov Petr , Makarychev-Mikhailov Sergey , Roye Justin
Published: January 2020

Successful cement placement in horizontal wellbores requires solutions for several technical challenges. Zonal isolation provided by cement is considered an important factor for efficient stimulation. A cement system was designed and recently introduced in unconventional developments to mitigate hydraulic isolation challenges encountered when cementing horizontal wellbores. Herein, we disclose recent results that show the efficiency of the interactive cementing system (ICS) in both laboratory and field case studies. Specifically, decreased communication between stages and improved production compared to offsets.

At the 2018 SPE Annual Technical Conference, Kolchanov et al. described the ICS improving zonal isolation in wells that would otherwise contain mud channels symptomatic of the cleaning methods used in unconventional developments (SPE-191561-MS). The scaled performance tests disclosed in that publication are further evaluated to build on the relationship between the laboratory test and realistic downhole scenarios. Literature data indicate that >30% of stages have communications with previously treated zones. The ICS was shown to eliminate interstage communication during stimulation operations when compared to conventional cement systems. To investigate the effect of the ICS on completion quality, five wells cemented with the ICS and stimulated by multistage hydraulic fracturing were compared with numerous offset wells drilled, cemented, and stimulated during the last 2 years in the same producing zone within a 10-mile radius. The early normalized production data have been analyzed, and they indicate a statistically significant increase of production for the ICS-treated wells. This shows the importance of an integrated approach in well construction process, especially for challenging horizontal wells.
Unique Real-Time Production Optimization Methodology for a Multilateral Well with Hydraulic and Electrical Inflow Control Valves
Authors: Shaker Wisam , Majed Shammari , Ali Alshuwaikhat , Farooq Qazi
Published: January 2020

Productivity index is directly proportional to the total reservoir contact (RC) within the same reservoir, hence the applications of multilateral. The new generation of the smart multilateral well completions divides the lateral section into a number of segments through oil swell packers and integrated stations are placed against each segment. The integrated station consists of electrical ICVs, downhole liquid metering and WC and pressure measurements sensors.

The unique smart completion allows real-time optimization and controlling of unwanted fluids on the segment level, utilizing the fast electrical ICVs. A comprehensive methodology has been developed to establish a production balance across the segments and the laterals utilizing the real-time downhole measurements rather than the tedious trial and error procedure. The methodology is divided into two components. In the first component: a single multilateral model is constructed using a single well modelling application. The model is calibrated to match the recent production test conducted on the well. Several simulation runs are performed on the calibrated model to test different combination of the downhole and surface choke settings until the well produces the target on condition that the laterals are producing equally. In the second component: a real-time production optimization process is conducted on the well using the outcomes of the first component.

The proposed methodology was conducted on multilaterals well with hydraulic and electrical ICVs and successfully completed. A good match has been obtained between the outcomes of the calibrated modeland the actual performance of the well. The process brings the well to the target at a straightforward task and avoids the trial and error procedure that may.
Unleashing Industry 4.0 Opportunities: Big Data Analytics in the Midstream Oil & Gas Sector
Authors: Eissa Haidy
Published: January 2020

Besides the rapid developments in the energy industry, there are substantial technological advances. The energy industry is calling for a strategic transformation of operation and business models through digital technologies and integrated data flows, a process known by "Digitalization". Digitalization, which is driving the fourth industrial revolution (Industry 4.0), is one of the main drivers that are re-shaping the global energy landscape. Among the main pillars of Industry 4.0 are, Industrial Internet of Things (IIoT), Cloud Computing, Augmented Reality, 3D printing, Mobility Solutions, and Big Data Analytics. Given these technology trends, assets and processes have become smart, data-driven and integrated. Data is the foundation of digitalization, and to get on the right digitalization path, data silos that are streaming at very high rates from diverse sensors and systems, shall be firstly sorted out. Big data analytics helps in creating a bridge between raw data and knowledge. It unlocks hidden patterns and associations within massive raw data sets, that reveal new opportunities for better outcomes. The future energy landscape is shifting towards a data-driven decision making business culture. A successful implementation of Big Data analytics within oil and gas midstream operations, reduces non-productive time, achieves efficiency gains, and maximizes profitability. The great advancements in data science, cloud platform and computing power, are unleashing the potential of Big Data analytics. This paper focuses on the influence of Big Data on the midstream oil and gas sector. The paper begins with an overview on Big Data and its potential within the oil and gas industry. Thereafter, it reviews different applications of Big Data analytics in midstream operations. Finally, the paper discusses the challenges posed by Big Data analytics implementation, and discusses the measures that shall be taken to guarantee its successful implementation within the midstream sector.