bluuebunny/crossref_metadata_2025_split huggingface dataset:
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.
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.
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.