bluuebunny/crossref_metadata_2025 huggingface dataset:
Improved Reservoir Mapping by Integrated Interpretation Approach Using Horizontal Wells
Authors: Mergany M. I. , Narino A. R.
Published: October 2023

Reservoir maps are essential input data for drilling operations and 3D reservoir modeling. While drilling, these maps and petrophysical data guide the horizontal geo-steering, allowing real-time reservoir structure adjustments. In 3D modelling, they are commonly used to control the reservoir properties extrapolation and lateral extension. This study proposed a best practice approach to improve reservoir maps by using seismic reference interpretation and interpreted geologic control-points linked to horizontal well data. The proposed methodology involves the interpretation of a reference horizon from a 3D seismic volume, followed by a velocity model construction and depth conversion iteratively. The first iteration of the velocity model uses the interpreted seismic horizon along with sequence stratigraphic boundaries, as interpreted from core descriptions. Next, the log signature associated to each sequence boundary in the cored wells, is applied to the log data acquired in the vertical and deviated uncored wells. Finally, the horizontal wells are added to the correlation, to define additional stratigraphic markers, that allow the calculation/interpretation of control points applied to the reference horizon, capturing the relative position of the well within the target layer. The combination of the stratigraphic markers with the interpreted control points, together with the interpreted seismic horizons, results in a structural framework for the entire reservoir including most of the available data, maintaining the velocity model updated in a short-time basis. This data integrated approach, is producing high quality reservoir maps, representing subsurface structures more accurately than conventional mapping methods. The iteration of horizontal drilling and inter-well control points in the velocity model building process reduces the uncertainty in the depth conversion (from 50 ft. to 10 ft.). Regarding that the velocity model algorithm cannot use the inter-well points directly, because they are not associated with actual well markers of the reference horizon. The study adopted a workaround that involves the adjustment of the seismic reference horizon following the spatial distribution of the control points, to iterate the velocity model workflow, updating it with real-time data in the study area. Our method improves reservoir mapping compared to the conventional workflow. It is used to build robust 3D velocity models as well as 3D geological models over stacked reservoirs by using multiple seismic reference horizons, stratigraphic boundaries, well log picks, and vertical seismic profiles (VSP) data. The inter-well points from horizontal wells use the relative position of the well, solving the lack of markers in the area between wells.
Unlocking the Value of Cloud-Enabled Emission Monitoring Systems for Sustainable Operations in the Oil and Gas Industry
Authors: Saboo S.
Published: October 2023

The oil and gas industry plays a pivotal role in meeting global energy demands, but it faces increasing pressure to address environmental sustainability (Dahai Zhang 2017). The importance of sustainability metrics has surged in recent years, prompting senior leadership to optimize financial, operational, and environmental aspects. The global pandemic further accelerated this focus on sustainability, emphasizing the need for accurate emission measurement, transparent modeling, and informed decision-making based on ESG data (Niu 2022). To address these challenges, this paper explores how industry leaders can unlock the value of cloud-enabled Emission Monitoring Systems (EMS) to achieve sustainability goals and generate additional value.

Cloud-enabled EMS, empowered by advanced technologies, provides innovative solutions for monitoring and managing emissions, offering real-time insights and facilitating data-driven decision-making (Uyanik 2019). By leveraging the power of the cloud, energy companies can harness advanced analytics, automation, and scalable infrastructure to enhance their environmental performance and contribute to a more sustainable future.

This paper delves into industrial trends and challenges faced by leaders in emissions monitoring, examines how digitalization can overcome these challenges, and highlights the latest developments in cloud-enabled EMS from a business perspective. It also features a comprehensive case study showcasing an independent oil producer’s journey in the United States, illustrating tangible benefits achieved through the adoption of cloud-enabled EMS technologies.

By analyzing the benefits, challenges, and recommendations associated with EMS and cloud-enabled solutions, this paper aims to contribute valuable insights for the oil and gas industry. It showcases how these technologies can enhance emissions monitoring and reporting, improve data analysis and decision-making, and lead to significant cost savings. Ultimately, the paper demonstrates the value cloud-enabled EMS systems can add to sustainable operations in the oil and gas industry. By addressing challenges and fostering collaboration, energy companies can drive sustainability, achieve their environmental goals, and contribute to a greener and more resilient energy industry.
Seismocracks: Tectonic Origin of Stylolites, Abu Dhabi Carbonates
Authors: Noufal Abdelwahab , Alshehhi Hamda
Published: October 2023

Stylolites have been defined as irregular planes of discontinuity at the bedding boundaries caused by pressure solution, which leads to concentration of relatively insoluble materials of the surrounding/enclosed rocks. Factors controlling stylolite formation, and the relationship between stylolites and the porosity of the enclosing reservoirs were subject of many studies. The published literatures identified the pressure solution as the carbonate diagenesis that influenced by mechanical compaction, chemical changes, water flow and cementation. The pressure extension within the carbonates is affected by the grains size, shape, and type, in addition to the clay content and temperature. Others adding the role of stress, which is responsible for stylolites development and distribution. This paper justifies, the tectonic origin of stylolites.

The stylolites were studied in many carbonate reservoirs of Abu Dhabi fields and comparing the cores with extensive outcrop studies. The collected data are also investigated from the water zone, on the flanks of the structure with cores from the oil zone, on the crest. The main controlling factor of stylolites initiation and propagation is at the tectonic boundaries and tectonic events showing its tectonic origin. The present study addresses a unique approach to solve this feature and hence understanding its impact on hydrocarbon flow, migration and trappment. The development of stylolites creates a reduction in the porosity, permeability and the bed thickness of the reservoir, the latter were not observed in Abu Dhabi as there is no bed thickness reduction were addressed.

Stylolites are principally serrated surfaces formed due to tectonic stress events and their amplitude is dependent on the stress dissipation stages. In addition, it is found some stylolites played as microfaults, which justifies the tectonic origin. This contradicts the previous interpretation, which justifies the needs of compaction, although compaction needs weight of overburden, while the pressure solution process happening in shallow depths. As the fluids migrates, two situations arising, the first is when the maximum stress is vertical, where along the serrated shearing surfaces results in dissolving and accelerating the crests left behind leading to dissolution propagation. The second when the maximum stress is parallel to the bedding, leading to slowing down the crests propagation left behind. In case, the bedding planes are not sheared enough, the connectivity is very less leading to trapped fluid, which facilitate dissolving and penetrating the serrated areas along the minimum stress direction.

The results should be relevant to unravel and predict: the spatial and temporal distribution of Seismocracks and their roles in reservoir performance and production. The Seismocracks are thus of first-order importance for reservoir performance.
Integrating the Dielectric and NMR Techniques to Investigate the Petrophysical Properties of Sandstone and Carbonate Formations
Authors: Oshaish Ali , Elsayed Mahmoud , Hassan Amjed , Al-Ofi Salah , El-Husseiny Ammar , Mahmoud Mohamed
Published: October 2023

The nuclear magnetic resonance (NMR) and the dielectric dispersion are excellent techniques to study the petrophysical properties of hydrocarbon bearing rocks. The superiority of these measurements rises from their sensitivity to the different interactions occurring in the porous medium which include grain – fluid and fluid – fluid interactions. Therefore, an integration between both measurements helps in evaluating different petrophysical properties such as mineral composition, porosity, permeability, wettability and fluids distribution.

In this study, we have explored the dielectric and the NMR responses to pores structure, permeability, and saturation profiles for two sandstone samples (Berea and Fontainebleau) and two Indiana limestone samples with different permeability values which were undergone a centrifuge drainage. Initially, routine core analysis was performed on the samples including porosity, permeability XRD and Scan Electron Microscope (SEM). The rock samples were studied at 3 saturation points, dry condition, 100 % water saturation (Sw=1) and irreducible water saturation (Swirr). To reach the Swirr conditions, the sample was undergone a drainage cycle with model oil using the centrifuge.

The results showed an excellent correlation between NMR, dielectric measurements and the petrophysical properties. The small pores and the lower permeability (21 md) of one of the carbonate rock compared to the sandstone samples (74 md for Fontainebleau and 100 md for the Berea) affected the sweep of the water during the centrifuge process which resulted in a variation in the saturation profile confirmed by the NMR saturation profile and spatial T2 techniques and higher Swirr value (43%) in the carbonate sample while the sandstone samples showed uniform fluid distribution and less Swirr (26% and 9.51% for the Berea and Fontainebleau samples respectively). Consequently, a significant variation between the dielectric measurements of the inlet and the outlet was observed in the carbonate sample with low permeability. Overall, this study provides a new workflow of integrating the dielectric and the NMR techniques to assess the impact of petrophysical properties such porosity, permeability, and pore sizes on the saturation profile across the sample resulted from the centrifuge displacement which consequently affect the dielectric and NMR responses. Ultimately, this work would help in improving the characterization of carbonate and sandstone formations by integrating the dielectric and NMR measurements.
Rapid High-Fidelity Forecasting for Geological Carbon Storage Using Neural Operator and Transfer Learning
Authors: Falola Yusuf , Misra Siddharth , Nunez Andres Calvo
Published: October 2023

Carbon sequestration is a promising technique to minimize the emission of CO2 to the atmosphere. However, the computational time required for CO2 forecasting using commercial numerical simulators can be prohibitive for complex problems. In this work, we propose the use of transfer learning to rapidly forecast the CO2 pressure plume and saturation distribution under uncertain geological and operational conditions, specifically for variations in injector locations and injector rates. We first train a Fourier Neural Operator (FNO)-based machine learning (ML) model on a limited set of simple scenarios. Then, we use transfer learning to fine-tune the FNO model on a larger set of complex scenarios. Most importantly, the CMG forecasting time for one scenario requires approximately 40 to 50 minutes, which was drastically reduced to 12 seconds by using Fourier Neural Operator and then reduced further to 8 seconds by implementing transfer learning on the Fourier neural operator. The mean relative errors of the neural operator predictions of pressure and saturation were 1.42% and 7.9%, respectively. These errors get slightly higher when transfer learning is implemented on neural operator to learn complex task with less amount of data and low training time. Our results show that transfer learning can significantly reduce the computational time required for CO2 forecasting. The data generation and model training times were reduced by 50% and 75%, respectively, by using transfer learning on the Fourier neural operator. Additionally, the total number of trainable parameters was reduced by 99.9%. Our results demonstrate the potential of transfer learning for rapid forecasting of CO2 pressure plume and saturation distribution. This technique can be used to improve the efficiency of CO2 forecasting and to help mitigate the risks associated with CO2 leakage.
The Challenge to Integrate Sedimentology and Well Completion to Predict Production Performance in an Unconventional Reservoir
Authors: de Melo Ricardo C. B. , Mykhaylenko Sergiy , Hosur Laksmikantha Rao
Published: October 2023

This study presents the integration of sedimentology and well completion to explain variable production performances in wells from an unconventional siliciclastic reservoir. The significance of our study is that multidisciplinary approaches are among the most comprehensive solutions that can forecast areas with different performance and, thus, make consistent completion strategies before drilling and optimize the development of unconventional reservoir acreages.

The main producing interval within the area of interest includes cm-thick, carbonate-rich layers that based on a total of 20 vertical wells are inferred to extend several km. The cumulative thickness of these layers and their frequency (number of layers/thickness of producing interval) vary across the area. This variation is compared to hydraulic fracturing and completion data that include breakdown pressure, pressure decline, hammer effect, ISIP gradient, fracture driven interactions, and completion strategies (i.e., proppant intensity, volume of fluids, cluster, stages, well spacing). All these data are then compared to the reported production from horizontal wells (> 150).

Both the correlation of vertical wells and the distribution maps predict the existence of higher cumulative thickness and frequency of carbonate-rich layers towards the center of the study area. Also, the frequency of carbonate-rich layers reveals a positive relationship with the normalized production of nearby horizontal wells, which points to increased production as brittleness of the system increases. However, a variable degree of data dispersion is seen in this positive relationship, and it is attributed to different completion strategies over the years. One way, though, to explore the relationship between completion parameters, sedimentology, and well production is to use a correlation matrix that allows to differentiate the relative importance of each factor. For instance, we observe a strong and negative correlation between the frequency of carbonate-rich layers and the ISIP gradients, whereas the ISIP gradients and the well production exhibit a small, albeit positive, correlation. Completion and hydraulic fracture designs undoubtedly influence well production but there exists an inherent anisotropy, related to the presence of cm-thick, fragile, carbonate-rich layers between m-thick, plastic, clay-rich intervals, that affects the toughness of the system and, ultimately, impacts well production.

This work presents solution seeking through integration of multidisciplinary approaches with application of data analytics to create predictive methodologies. The use of two different disciplines such as sedimentology and completion design, rarely integrated together for an unconventional play, is novelty by itself. Correlation matrices could play a fundamental role in the optimization of field development plans in general and bespoke hydraulic fracture design.
A Novel Approach for Improved Accuracy in Water Volume and Reservoir Quality Assessment During Field Development in Organic Shales
Authors: Bustos U. D. , Bernhardt C. , Vera L.
Published: October 2023

Complexity in petrophysical interpretation in organic shales is related to the presence of inorganic and organic pores that house hydrocarbons, kerogen, and water. Advanced workflows were proposed for assessing hydrocarbon types and reservoir maturity. The water volume and saturation assessment are routinely done with archie equations, using resistivity, nuclear and/or sonic measurements. With proper matrix corrections on porosity computation and representative inputs of formation water salinity (Rw), wettability (N) and tortuosity (M), water saturation can be calculated. In organic shales, however, the archie equations parameters can be highly variable across a formation and, a well-known thermal maturity effect on resistivity logs (higher maturity tends to cause a drop in resistivity), leads to incremental uncertainty on formation water assessment.

During field development stage in Vaca Muerta formation, Argentina, a complete electrical logging acquisition is carried out in pilot wells. With this information, landing zones for placing the horizontal-producer wells are defined. Since the criteria for landing zone definition is strongly conditioned by water saturation, we focus on uncertainty reduction in water volume estimation as a first step and then calculation of a new practical reservoir quality indicator that matches local production behavior.

Given the independence to archie equations and similar depth of investigation, dielectric dispersion and nuclear magnetic resonance logging became of increasing interest for water and hydrocarbons volumes and saturation computation. In Vaca Muerta shale oil targets, we developed an integrated method based on dielectric dispersion, magnetic resonance, formation capture cross section and spectroscopy-derived formation chlorine for early assessment of representative water volume and saturation for best landing zone selection with producible hydrocarbons and minimum free water.

We built a matrix model from spectroscopy dry weights, nuclear and NMR logs, solving for minerals, kerogen, and organic/inorganic matrix-corrected porosity. With a clustering technique applied on data from a new high-resolution NMR T1-T2 processing and from Dielectric inversion, with inputs from total formation capture cross section and spectroscopy derived chlorine dry weight, we obtain two independent water volumes. Through iterative process, a representative water volume is achieved whenever differences are below two porosity units. Then, incorporating inorganic/organic corrected porosity, formation total water saturation is derived. Additionally, by splitting volumes of pore water and hydrocarbon in both organic and inorganic pores, we compute a continuous reservoir productivity index that considers both producible hydrocarbons and combination of bound oil and inorganic pore water.
Digital Transformation Thru MPD Automation
Authors: Ziadat W. , Kadadha A.
Published: October 2023

Digital Transformation through MPD Automation is revolutionising the oil and gas industry. Digital technologies provide access to higher-quality information about operations performance and allow companies to react quickly when unexpected events occur due to weather or equipment malfunctions/failures. Intelligent MPD (Managed Pressure Drilling) is an advanced form of Digital Transformation that applies sophisticated algorithms to optimise drilling operations. It takes full advantage of Big Data Analytics in order to maximise safety and performance during drilling operations by controlling pressure and flow rate, learning from machine twins, and providing greater insight into their operational performance.

Intelligent MPD's ability to monitor real-time variables makes it a safer alternative as it enables faster response times in various situations that arise during the drilling process without manual intervention from personnel onsite. Digital transformation with MPD automation also helps reduce costs associated with manual labour as it requires fewer people for operational tasks and allows for more efficient use of resources (Elvin, 2023 and Sanchez, 2022).

Other benefits of Digital Transformation through MPD Automation include improved accuracy in decision-making and greater transparency in operations processes. Advanced algorithms mean companies can make decisions based on reliable, up-to-date data rather than relying solely on human judgement, which can lead to mistakes or misjudgment, which can be costly. Additionally, Digital Transformation provides better visibility into the current state of the wellbore at all times, allowing for quicker response times when unforeseen circumstances occur. This drastically improves safety protocols and reduces potential risks for personnel onsite.
Gamification as a Tool to Boost Employees Engagement
Authors: Dubois A. , Kelamcherry B. V.
Published: October 2023

Gamification, the application of game elements and mechanics in nongame contexts, has emerged as a compelling approach to learning. It taps into our natural inclination for play and competition, and it is transforming learning into an enjoyable experience. Gamification is a game changer, as it boosts engagement, increases motivation, provides a meaningful experience, and accelerates the adoption of technology. This transformative approach revolutionizes training, making it more effective, interactive, and inspiring, while nurturing a lifelong love for learning and growth.

This paper provides an in-depth examination of the development of two distinct games and their practical integration into training deliveries. It explores the advantages of incorporating games into the learning process, considering both the student's perspective and the corporate viewpoint.
Application of Distance Based Generalized Sensitivity Analysis and Bayesian Inversion Method in Simulation of a Giant Fractured Carbonate Reservoir with Unstructured Grids
Authors: Fang Junling , Li Chen , Shi Wen , Yang Tao , Wang Hehua , AL Marzooqi Maitha , Al Ansi Rasha , Zhang Liang , Hu Xuezhi , Xu Jiafeng , Zhou Shuisheng , Xu Fengqiang
Published: October 2023

Multi-scale natural fractures bring challenges in geological modeling and flow simulation of carbonate reservoirs. History matching is extremely difficult due to significant heterogeneities and uncertainties, especially for those wells identified as "dominated by fractures". A novel, systematic approach is applied to model fractures explicitly, to perform flow simulation efficiently and eventually to match the production history accurately.

First, a discrete model using unstructured triangular grids were built to fully resolve the geometry and distribution of large-scale fractures. Then the contribution of small-scale fractures was modeled using flow-based upscaling algorithms to yield enhanced porosity and permeability of matrix grid cells. Finally, the connectivity list was calculated for each pair of matrix-matrix, matrix-fracture, and fracture-fracture cells for flow simulation. Then the Distance-based Generalized Sensitivity Analysis (DGSA) method is applied to evaluate the sensitivity of the uncertain parameters in the reservoir model. Conditioning with the well production history as "given" information, the Bayesian inversion method is employed to reduce the uncertainty of fracture properties including exact position, length, and permeability etc.

The entire workflow/approach was applied to a gigantic, naturally fractured reservoir with multi-billion-barrel oil reserves in Middle East. More than five hundred large-scale fractures are characterized in the simulation model explicitly using triangular prism grids. The resulted simulation model contains over 800,000 unstructured cells. It takes only one hour on a single CPU core to simulate the entire production history of over three decades for more than 100 production wells. The high simulation efficiency facilitates sensitivity analysis and history matching in which more than one thousand cases are simulated. In the meantime, due to the explicit representation of large-scale fractures, the rapid water breakthrough in some of the producers could be captured much more accurately than standard dual-porosity dual-permeability (DPDP) models. In the history matching process, the uncertainties of the sensitive parameters including most fracture and some matrix properties are systematically reduced following the Bayesian inversion method. The history-matched fracture network and matrix properties provides an accurate and efficient simulation model for future prediction and infill well optimization.
Time-Lapse FWI Using Borehole Seismic Data for CO2-EOR Monitoring in a Carbonate Field Onshore Abu Dhabi
Authors: Yamada Y. , Nakayama S. , Ishiyama T. , Mochizuki M. , Mouri T. , Shimizu H. , Aljaberi A. A. , Al Hamedi A. M. , Mohamed M. , Ahmed J. , Waqas M.
Published: October 2023

Time-lapse seismic plays a vital role in monitoring the dynamic behavior of reservoirs during carbon dioxide enhanced oil recovery (CO2-EOR). However, its application to carbonate fields onshore Abu Dhabi presents several challenges, necessitating appropriate datasets and analysis tools. For proper understanding of fluid behaviors in a carbonate field onshore Abu Dhabi, the time-lapse vertical seismic profiling (VSP) datasets were acquired with an optimum signal-to-noise ratio. Firstly, pre-processing was performed on baseline and monitoring surveys, resulting in improvement of data quality and repeatability in a quantitative manner. We then applied full waveform inversion (FWI) to estimate time-lapse velocity changes induced by CO2-EOR. After the parameter optimization, the inverted velocity showed updates in the correct direction while minimizing the data residual. To stabilize and optimize the time-lapse FWI application, three different approaches were performed: a parallel approach, a common model approach, and a target-oriented approach. As a result, the time-lapse velocity changes near the injection well were clearly recognizable in the reservoir of interest. This has suggested that time-lapse property changes associated with CO2-EOR operation can be captured through time-lapse FWI. Therefore, time-lapse borehole seismic imaging in conjunction with the applied FWI scheme can be an effective tool for precise monitoring of fluid behavior in a carbonate reservoir during CO2-EOR operations.
Next-Generation Drilling Fluid Management: Real-Time Monitoring and Autonomous Control
Authors: Zhang Z. , Rached R. M. , Xun Y. , Tian J. , Wang Z. , Fan S.
Published: October 2023

Accurate measurement and control of drilling fluid properties are crucial for safe and successful drilling operations. Traditional manual methods are slow and require significant manpower, underscoring the need for real-time monitoring. This paper highlights a new online system for monitoring drilling fluids, enabling intelligent control of drilling fluid performance. The online monitoring system improves drilling industry safety and success by measuring accurate, timely, and automated properties of drilling fluids.

The online drilling fluid monitoring system continuously measures ten essential parameters, including apparent viscosity, plastic viscosity, yield point, density, pH, and ion concentrations. The system uses a newly-developed variable diameter pipe technology to measure viscosity, and a patented double-pressure vibrating tube technology to measure density. Ion-selective electrodes enable fast and accurate ion concentration measurements, eliminating cumbersome titrations. The novel methods provide a quick measurement (<1s sampling rate) with a wide range and minimal errors. The system is modular and includes sampling, measurement, data acquisition, control, and pressure protection subsystems, with software integration for data visualization and remote data transmission to predetermined data centers.

The system achieves one measurement every second, providing real-time and continuous monitoring of 10 essential parameters. With 30 equipment sets produced and trialed in 200 wells, the system has demonstrated continuous operation for over 125 days. The online, real-time, and continuous monitoring capabilities offer several benefits, including improved data quality and frequency, reduced on-site labor and associated health and safety hazards. Notable field applications of the system include the automatic detection of the mud density anomaly in a critical well with a narrow safe mud density, and remote real-time transmission of data for integrated geological logging analysis. Further development will focus on automating error detection and incorporating additional sensors for multiphase fluids.

This paper highlights a new online drilling fluid monitoring system that improves safety and success in drilling by providing accurate, timely, and automated performance control. The system measures ten essential parameters utilizing patented technologies and provides a continuous profile of the full set of properties with time. Field trials have demonstrated the advantages of this technology, including remote data transmission and automated error detection. The system reduces on-site labor and associated safety hazards, improves HSE on-site, and opens the door to oilfield data analysis automation.
Enabling Contextual Natural Language Search on Oil and Gas Databases
Authors: Lokhande Avinash , Pillai Prashanth , Mangsuli Purnaprajna
Published: October 2023

The oil and gas (O&G) industry generates a significant volume of data from various sources, such as seismic surveys, well logs, drilling reports and others, which are stored in either relational or non-relational databases. However, searching for pertinent data records requires the end-user to be conversant with database query syntaxes and schema definitions, which is challenging. In this paper, we introduce a novel framework to interact with O&G databases using natural language. We trained a Text-to-Text Transfer Transformer (T5) (Raffel, 2020) in a multi-task setting incorporating natural language to structured query language (Text-to-SQL) conversion as a main task with query context classification, and passage context classification as auxiliary tasks. We also introduce a method for data augmentation with SQL to natural language task (SQL-to-Text). Additionally, we implemented database-aware query disambiguation for typos and spelling correction by incorporating string and phonetics similarity algorithms. We evaluated the performance of our trained language model (LM) on a test dataset consisting of 1711 natural language queries involving diverse O&G entities, such as fields, wellbore, well logs, markers, etc. Our innovative multi-task approach resulted in a remarkable performance, achieving an exact set match accuracy (EM) of 88.5% on the Text-to-SQL task with impressive F1 scores of 96.3% for query context classification, and 87.7% for passage context classification tasks. We observed multi-task training of LM improved the EM accuracy of the SQL predictions. We also demonstrate the application of our approach to enable contextual natural language search on Open Subsurface Data Universe (OSDUTM) platform. Overall, our approach has potential to transform how end-users search and retrieve data from O&G databases.
Experimental Investigation of Wax Deposition in Model Oils Under the Influence of New Eco-Friendly Wax Inhibitors
Authors: Sharma Rohit , Pattnaik Anuesh , Kar Pratik Raj , Singh Prabh Preet , Tiwari Abhigyan , Deka Barasha
Published: October 2023

This research work focuses on the evaluation of novel and eco-friendly additives as pour point depressants for crude oils to improve the flow properties. Neem oil, rogan kahu, rogan khashkhash, kalonji oil and mahua oil were tested for the pour point studies on a model oil containing two different types of waxes. The study reflected that rogan khaskhas beneficiated crude oil showed highest depression in pour point i.e. up to 6 °C and 10 °C in Type 2 and Type 3 wax embedded base oils respectively, indicating the best additive amongst the five additives tested. Neem oil and rogan kahu also showed encouraging performance in Type 3 wax embedded base oil, where pour point depression was observed as 9 °C by both the additives. Mahua oil and kalonji oil are found to be very less effective in the model oil samples containing the two types of wax. Further beneficiation studies were performed for rogan khaskhas, neem oil and rogan kahu considering their pour point beneficiation which indicated viscosity reduction and wax structure modification for the three additives beneficiated oils. From the results of pour point, viscosity and polarized microscopy experiments, it was observed that rogan khashkhash performance was the best amongst the tested additives as pour point depressant and flow improver.
Energy Efficiency Improvement Technique in Industrial Cooling Tower in Large Petrochemical Complex - Case Study in Sidpec
Authors: Salaheldin M. , Subramanian V. , Salah M.
Published: October 2023

Decarbonization became a crucial topic in all industries especially those working in the energy sector in order to reduce greenhouse gas emissions and mitigate the climate change threat. Energy efficiency has a significant impact on decarbonization, this was clearly announced during COP27, when the Egyptian Ministry of Petroleum and Mineral Resources has announced the energy efficiency strategy in Egyptian Energy Sector during one of the thematic days that was called "Decarbonization Day" and all the companies started to prepare energy efficiency improvement strategy that is aligned with the strategy of the corresponding ministry.

In this paper, an integrated approach for energy efficiency improvement in industrial cooling towers to maximize process and production efficiency, a case study in Sidpec, one of the giant petrochemical plants in Egypt is introduced. The cooling tower optimization is one of the identified opportunities in the company's energy efficiency improvement roadmap which is the backbone of the company decarbonization strategy.

Demand for cooling water has become more pronounced, both in winter and summer because of climate change, and a growing number of warm days which cost the industry millions of dollars annually. In this study, energy consumption issues were resolved in cooling tower in an efficient way with zero capital cost investment.

A 4-step approach for improvement opportunities was applied and was supported by a developed cooling tower simulation model. This approach was successfully implemented and reached the right decision for how a cooling tower optimization and considerable cost reduction can be achieved.
Time-Lapse Inversion of Velocity and Q Factor
Authors: Chen Jianming , Zhao Yansheng
Published: October 2023

Rock properties play a crucial role in influencing the propagation of seismic waves within the Earth, providing valuable insights into various aspects such as the presence of hydrocarbons, porosity, saturation, and permeability variations. Seismic imaging serves two main purposes: mapping the heterogeneity of underground reservoirs and monitoring recovery processes on-site (Nur, 1989). Over time, both production and injection activities alter the response of the reservoir. Time-lapse (or 4-D) seismic monitoring is an essential tool for reservoir management, enabling the tracking of displacement processes. Changes in seismic properties, such as velocity and quality factor Q, can offer quantitative estimates of fluid changes.

Some rock properties related to fluid saturation in the reservoir, such as anelastic absorption quantified by the quality factor Q, can undergo more significant changes than others, such as oil and gas mixed with brine or CO2. To estimate the quality factor Q from seismic reflections, we utilize a tomographic inversion algorithm based on the frequency shift method (Quan and Harris, 1997). This algorithm generates a macro-model for anelastic attenuation. In order to enhance resolution and obtain a micro-model, Poggiagliolmi and Vesnaver (2014) propose a new algorithm that is stable and reliable for computing the instantaneous frequency, which represents the centroid of the instantaneous spectrum. This algorithm improves the stability of calculating the instantaneous frequency and provides a high-resolution image of anelastic absorption. By utilizing this approach, the resulting broadband image of anelastic absorption in depth can offer superior monitoring of reservoir changes compared to conventional methods.
Screening and Evaluation for Asphaltene Dispersants for Hassi Messaoud Field-Algeria: Laboratory Tests
Authors: Djamila Maatallah , Zohra Benaceur Fatima , Ferreira Daniel , Mahmoodaghdam Elham
Published: October 2023

Precipitation and deposition of organic solids such as asphaltene are very common issues for the petroleum industry globally. The deposition of these solids is considered one of the main issues impacting production losses throughout the pore to refinery chain.

Solids induced by asphaltene instability frequently led to well, pipeline and surface equipment plugging, requiring frequent production stops and large -scale and costly operations for removal and to normalize production.

Oil producers develop different prevention-/-mitigation strategies depending on reservoir characteristics; In the case of Hassi Messaoud oil field located in Algeria, asphaltene precipitation and deposition is a recurrent issue from the start of production. The most common solutions adopted are physical removal by scraping the production tubing(slickline) and solvent washes of the near-wellbore area and tubing to maintain or recover production. These options are time consuming and costly and have efficiency limitations, so looking for more efficient solutions has become a priority. Chemical solutions are among the techniques being investigated.

This paper describes the work done evaluating different chemical dispersants as asphaltene treatment for a Hassi Messaoudi oil field. Many commercial asphaltene dispersants were selected and submitted to the laboratory for evaluation to determine their performance and efficiency at reservoir conditions (instead of stock tank oil conditions) to accurately recreate reservoir challenges for proper chemical evaluation.

Work was initiated by performing deposition tests at ambient conditions using various dosages of each chemical, on dead oil samples. Chemicals performance is estimated based on deposition reduction compared to a control with no chemical addition. While these are accepted in industry as quick screening tests, they do not reproduce the complexity of downhole conditions, especially considering the effect of solution gas into asphaltene stability. Therefore, for better understanding of dispersant behavior, laboratory tests were conducted on live oil at specified conditions of temperature, pressure, and shear using Real View®. Prior to that, all dispersants were tested for asphaltene stability at several dosages by a light-scattering technique that combines a solid detection system (SDS) with high-pressure microscopy (HPM).
Innovative Hydrocarbon-Based Additives for Diesel Engines in Oil Field Operations: Mitigating GHG Emissions, Improving Performance, and Advancing Sustainability
Authors: Alrefaai M. M. , Hashmi U. , AlJneibi M. S. , Alhebsi A. A. , Alkatheeri F. O. , A G. De Jesus Geurrero Pena
Published: October 2023

Over the years, in most countries, more strict regulations are being implemented on the emissions resulting from fossil fuels combustion. Among all the conventional fuels used, diesel is considered the one being highly polluting in terms of soot emissions. Therefore many researchers and organizations are continuously looking for technologies to reduce the concentration of particulates produced from diesel burning activities. One of the promising solutions is the hydrocarbon-based additives for Diesel fuel (DF) that can reduce the soot emissions and other harmful pollutants from diesel engine.

Diesel as a fuel and its applications

The fossil fuel mainly diesel and gasoline, are blends of hydrocarbons produced by fractional distillation of crude oil in petroleum refineries [1-3]. Based on the fuel used, the engines were classified into gasoline and diesel engines, which use gasoline and diesel fuels, respectively. Both are internal combustion engines, where chemical energy from the fuel is converted to mechanical energy. The main difference between the two engines is their mechanism of operation. In the gasoline engine, fuel and air are mixed first, and then, the mixture is compressed by pistons before ignition take place by spark generated from the spark plugs. On the other hand, the air is initially compressed in the diesel engine, before the fuel is injected, and then the autoignition of the fuel takes place in hot compressed air [4].

Despite of the fuel type, fossil fuels are generally witnessing an increase in their demand all over the world because of their high energy content and combustion efficiency, consequently, the level of pollutants in the atmosphere is rising as well [5, 6]. DF is mainly used due to their excellent drivability and high durability for many applications such as electric power generation, transportation (including road trucks, military, marine shipping and rail transportation) and off-road uses (e.g., mining, farming and construction) [7]. However, the main concern with the using DF is the pollution caused and its impact on environment [8]. Pollutants are formed as a result of the incomplete combustion in internal engines. Such as soot which known as particulate matter as well, carbon monoxide (CO), nitrogen oxides (NOx), sulfur oxides (SOx) and volatile organic compounds (VOC) [4, 9].
Comprehensive Correlation Analysis of Influencing Factors of Shale Gas Productivity Based on Adaptive Lasso Regression
Authors: Zhao Wentao , Wang Weiyi , Zheng Majia , Luo Di , Xie Mengke , Wu Ya
Published: October 2023

The shale gas demonstration area in the Sichuan Basin is currently at the stage of large-scale development. The main factors controlling the high production of shale gas wells has been a hot research topic in shale gas development.

To obtain the main influencing factors of shale gas productivity, the typical case well area(Z 201)was selected as an example. 12 geological and engineering productivity variables of shale gas well were optimized based on Adaptive Lasso Regression method. In addition, optimization of multiple linear regression model and weight analysis of key parameters affecting productivity were conducted. Finally, the main controlling factors of productivity of shale gas well were identified, and the corresponding productivity prediction equation of shale gas single well was established.

The results showed five independent variables (the drilling length of 1 sublayer, fracture density, thickness, brittleness and maximum principal stress) were optimized based on Adaptive Lasso regression, and the drilling length of 1 sublayer was the main factor controlling the high production of gas wells. In addition, the key to improving the shale gas well productivity were the natural brittle fracture development area and the low stress zone. Based on these study results, a production prediction equation for standardized testing of single well in this area was established, which may guide the drilling operation effectively and improve the production rate of shale gas wells.

The research results have certain guidance and reference significance for shale gas production capacity construction and development. It can provide an important basis for optimizing shale gas favorable areas and evaluating single well productivity.
Bit Wear Prediction Using Adaptive Data Analytics: Dynamic Scenario for Real Time Well Engineering
Authors: Samuel Robello , Liu Zhengchun
Published: October 2023

As the wells become more complex, identifying the locations of critical bit wear becomes increasingly difficult. The analytical models currently used in the drilling industry are not capable of completely realizing the downhole dynamics and thus incapable of bit wear prediction – they either overestimate or are too conservative, while some just require too many parameters, constants and coefficients. There is no acceptable universal mathematical model that describes bit wear and thus this paper proposes not to use a model at all. This study focuses on harnessing the impact of real time downhole dynamics on drilling parameters to predict bit wear using adaptive data analysis techniques.

Real time rate of penetration (ROP) is identified as a parameter that would demonstrate the impact of downhole dynamics with a high degree of functionality. Though other parameters can also be used, ROP is the basic parameter always available. Depending on the parameters available, different parameters can be convoluted together and fed to the algorithm. For instance, ROP and Gamma ray data can be convoluted in an appropriate way to predict bit wear and also predict whether the bit wear is due to formation change or not. The WITSML ROP and drilling data are continuously fed to an adaptive data analytics algorithm which decomposes the incremental ROP data at each depth into its intrinsic mode functions (IMF) using Empirical Mode Decomposition. The trend of the final IMF at each depth is continuously monitored to predict bit wear. For practical purposes of making the whole process fast and straightforward, the energy of the final IMF, referred as IMF energy, is also continuously computed.

In either case, the depth and/or depth interval where the IMF energy becomes negative and/or where the trend of the final IMF plot flips is the depth and/or interval where the bit wear is high and may lead to failure. The proposed workflow has been tested on historical wells and the results are compared with analytical models (B. Rashidi et al 2010 (more conservative) and Z. Liu et al. 2014) used in the industry. The results were also verified with the Daily Drilling reports from the field to confirm the predictions of the algorithm presented. The results predicted were synchronous. The paper presents field examples to predict and estimate the bit wear using actual data. Estimation of rate of penetration (ROP) using the new bit wear model was also carried out using actual field drilling parameters. The calculated ROP profile closely matched with the actual data within reasonable accuracy of less than 5%. Specific procedures are proposed for drilling parameter estimation and effective prediction of ROP and bit life.