bluuebunny/crossref_metadata_2025 huggingface dataset:
First Application of Wave Equation Based-AVO Inversion on a Carbonate Reservoir in UAE
Authors: Ahmed J. , Mahmood H. , Al-Naqbi S. , Aljaberi A. , Doulgeris P. , Gonza;ez H. , Zhang M. , Denitez P.
Published: October 2023

Carbonate reservoirs, by definition, present additional challenges compared to clastic sections in seismic reservoir characterization. The lower porosities often hinder the robust detection of fluids using traditional inversion methods, and the whole rock less sensitive to AVO (Amplitude Versus Offset). Additionally, many carbonate reservoirs exhibit property contrasts that greatly surpass the 0.1 reflection coefficient, where linear convolutional models best approximate the seismic signal effectively. The WEB-AVO Reservoir Characterization technology addresses key uncertainties in onshore carbonate reservoirs arising from inherent heterogeneities and lateral porosity variations, first because of the nature of the derived properties, which are the inverses of the Bulk Modulus (Compressibility), inverse of the Shear Modulus (Shear Compliance) and the Bulk Density. These properties serve as direct parameters for reservoir properties of interest. Notably, Compressibility being approximately three times more sensitive to fluids than Acoustic Impedance (Gisolf, 2016). enabling fluid detection in lower porosity ranges when compared to Acoustic Impedance. In addition, the nature of WEB-AVO, being based on the full wave equation and not the linearization of Zoeppritz equations, allows it to inherently handle the scattering seismic often present in carbonate sections. This includes interbed multiples and mode conversions, which can be a major challenge, especially with the land data. The technology also accounts for transmission effects. This minimizes the need for significant preconditioning for the inversion, as the scheme has the full physics required for acquired seismic data, as opposed to having to modify the acquired seismic data to fit the linear inversion models.

WEB-AVO was tested on onshore seismic data from the R Field, Abu Dhabi. WEB-IMI analysis was conducted to understand the scattering patterns in the subsurface and optimize the inversion window. The study aims at generating well-validated elastic properties that are directly related to reservoir properties spatially, using data that has not undergone gather preconditioning. These properties were later validated using the blind well test and its lateral predictability to capture the heterogeneity of the reservoir away from the wells.

This technique removes the small-reflectivity and primaries-only limitations of linear and reflectivity-based inversion schemes. Additionally, it enables the determination of elastic parameters that are more directly connected to rock properties of interest, such as porosity. In this geological setting, the ability to work on gathers in the presence of multiples has a significant positive impact on both cost and turn-around time.
New Downhole Wet-Mate Design Resulted in Capex Reduction
Authors: Almasri Abdulrakeeb , Joubran Jounathan , Alsadah Adnan
Published: October 2023

Intelligent completions in multizone wells require hydraulic control lines connected to the surface to maintain functionality. To reduce capital expenditure (Capex) of failed equipment, such as an electrical submersible pump (ESP), the industry required a reliable method to remove the upper completion without the complexity and uncertainty of unsetting one or more packers and pulling out the entire completion.

This paper will discuss the essential aspects of a new hydraulic wet-mate design, its qualification, and installation methods over numerous ESP wells combined with an intelligent completion that resulted in zero nonproductive time (NPT), with full control of the lower intelligent completion. Additionally, the future of this technology will be discussed, such as the addition of a direct-connect electrical wet mate that requires no orientation to the hydraulic wet-mate design for a complete two-trip intelligent completion.

The downhole wet-mate connector success is directly related to the design simplicity. The tool is concentric and requires no specific orientation for a successful wet mate. It also has the ability to run in highly deviated wells with a wide range of casing sizes without modifications from 7 into 9 5/8 in. Another crucial design feature is the soft-release mechanism that allows for the removal of the upper portion of the wet mate (male) from the lower portion (receptacle), without excessive overpull, such as overcoming a shear ring or snap latch. This ability allows the wet mate to be separated without excessive overpull that could damage the tool permanently. The qualification process included multiple iterations of pressure and temperature testing around the performance envelope, debris testing, and for the electrical portion, a test with high-salinity brine while recording the downhole pressure and temperature gauges.

De-completing an intelligent completion well can be complex, particularly when multiple packers require release. ESPs typically have a life span of approximately 5 years. The downhole wet-mate tool enables ESP replacement without disturbing the intelligent completion, which provides a significant advantage, especially in a multizone well equipped with multiple hydraulic-controlled interval control valves (ICVs).

Cost savings: Overall risk reduced by removing the need to retrieve the entire intelligent completion from the well and the re-spooling of additional control lines and related clamps and accessoriesStab in and stab out without rotation

Efficiency improvements: Soft-release device reduces the risk of system damage

This paper will discuss the aspects of a reliable wet-mate completion tool and highlight the concurrent successful installations that prove the design and deployment methods are sound.
Cost Effective Solution for Liquid Loading Elimination & Ensuring Production Sustainability
Authors: Maher Manar , Albadi Mohamed , Ahmed Zeeshan , Shaker Ashraf , Al Zarouni Abdulla , Al Bairaq Ahmed , M. Douglas Mahmood , Negi Rohit , Murphy Robert , George Joel , Brehme Moritz , Palomo Arturo
Published: October 2023

A cost-effective solution for liquid loading elimination and ensuring production sustainability was needed.

A liquid loading issue was identified by a client in UAE in one of their gas wells. Market scanning was conducted looking for a viable solution that could be implemented across fields. Liquid loading occurs when the gas velocity is insufficient to carry the produced liquid to the surface. The accumulation of the liquid in the wellbore produces astatic liquid column causing a decline in the well production or prevents it from producing over time.

A velocity string, a small-diameter tubing string run inside the production tubing of a well as a remedial treatment, was selected as a solution for this well. The velocity string works by reducing the flow area and increasing the flow velocity to enable produced liquids to be carried to the surface.

An evaluation of revised well performance was carried out for the gas-producing well before installing a velocity string in it. Well production data, fluid properties, and reservoir details were collected and production modeling through various sizes of velocity string was performed. It was deduced that a string with an outer diameter of 2.375-in. would provide a stable production rate minimizing periodic coiled tubing interventions. To overcome reduced fluid capacity through the velocity string, a sliding sleeve door was installed to allow annular flow enhancing the overall production volume.

The installation was performed by hanging the velocity tubing string with a thru tubing hanger inside the production tubing without the need to kill the well which eliminated formation damage issues.

The velocity string completion design allowed for accessibility to the existing safety valve, and it provided landing profiles for the installation of deep barriers or gauge hangers in the well in the future.

The well was put under production through the tubular section for its performance evaluation and the annular production through the sliding sleeve door was considered based on the evaluation.

The velocity string extends the continuous production life of the mature wells before plug and abandonment, or expensive workover operations are executed. Currently, the well is producing without any intervention for more than 16 months. Successful trial of the velocity string system in this candidate well as an economical and minimal footprint solution can be implemented across fields.

It also avoids any formation damage which occurs during well killing as velocity string installation is a live well operation and is a solution that can be designed to suit any well with various well environmentexperiencing liquid loading issues.
Riding the Wave: One-Touch Automatic Salt Segmentation by Coupling SAM and SegGPT
Authors: Koeshidayatullah Ardiansyah
Published: October 2023

Deep learning has transformed the way geological interpretations are conducted for subsurface energy exploration. Seismic image processing and interpretation are the most active areas where deep learning has been implemented to optimize the overall workflow. Among different seismic features, the identification and delineation of salt bodies often present a challenge in seismic interpretation. Salt boundary interpretation is important for understanding salt tectonics and velocity model building for seismic migration. Recent works have applied deep learning to help the identification of salt bodies with remarkable results. However, a large volume of high-quality labeled datasets is required to achieve good accuracy. Such a labeling task is costly, time-consuming, and prone to human error. This limitation hinders the progress of deep learning applications in seismic interpretation. With the rise of generative models, such as ChatGPT and zero-shot deep learning models, it is currently possible to train deep learning models with no or very minimum labeling and pre-training for a specific task. In this study, we utilized the recently introduced Segment Anything Model (SAM) to segment the salt bodies with only a few points or a line and coupled it with Segment Everything In-Context (SegGPT) for the surrounding features (i.e., non-salt bodies). The results show that the model could successfully identify and segment salt bodies with the one-touch method and show comparable accuracy with other conventional deep learning methods, achieving a mean intersection over union (mIoU) value of 0.85. For the first time, this study presents an application of combined generative and zero-shot models for seismic interpretation, particularly salt bodies identification. The proposed model has the potential to be applied to other features in seismic interpretation that would significantly optimize the process. The proposed model also allows the implementation of a greener deep learning model with a lower carbon footprint.
Novel Multi-Functional Chemistry to Maximize Performance in Paraffin Coated Acid Soluble Scale Cleanups
Authors: Rosanel Morales
Published: October 2023

Gradual diminution of the flow path of hydrocarbon in the near-wellbore area is actively linked to formation damage accumulation and a reduction in well productivity. Organic deposition in the formation and wellbore area can result from the use of hydrochloric acid (HCl) during acidizing, especially in the presence of free iron and organic liquids such as diesel, kerosene, or gasoline. Laboratory evaluation of the nature of the crude oil and stimulation fluids indicates the potential severity of the problem. This test is called acid sludge test.

Utilization of inorganic acid like HCl for inorganic deposits removal, like calcium carbonate and iron sulfide, has become one of the most common methods for well clean-up and stimulation. Unfortunately, due to the nature of the produced fluids (hydrocarbon), organic deposits like paraffin compounds under certain conditions, come out of solution, creating a coating on the inorganic scale, minimizing the performance of the acid job. Typical aromatic solvents utilized to address the organic deposits are not highly effective, as they are not fully miscible in the volume of the acid, and only dissolve a specific weight of paraffin before the solvent’s power is exhausted.

A multipackage formulation has been developed, fully miscible in acid, maximizing the performance of the scale dissolution, by effectively de-oiling and penetrating the organic coating layer build-up on calcium carbonate and iron sulfide scales in shorter soaking periods. This novel formulation cleans spontaneously by diffusion, breaking and solubilizing the organic deposits and providing the following additional benefits: Water-wets the surfaces (near-wellbore area, downhole equipment, tubing, and flowlines), including paraffin particles, preventing the re-agglomeration further down in the system.Improves formation oil mobility by reducing the capillary pressure in the formation.Prevents emulsion creation and acid sludge formation during acid jobs as formulation is fully miscible in acid.

The work in this paper studies the effect of this novel chemistry when it is used in acid jobs, and presents case history information on testing, chemical application, and subsequent field results across the Permian Basin in unconventional production.
Fingers of Fire: Understanding Hand and Fingers Injuries in Oil and Gas Industry
Authors: Kusumawati D. , Erwandi D. , Puspa W.
Published: October 2023

Hand and finger injuries are prevalent in the oil and gas industry, resulting in significant human and economic costs. Understanding the underlying causes and contributing factors of these injuries is crucial for their prevention and mitigation. This paper explores the use of human factors analysis and classification systems in comprehending hand and finger injuries in the oil and gas industry. It highlights the key elements of a comprehensive human factors approach, examines the major factors contributing to hand and finger injuries, and presents a classification system for analyzing and categorizing these injuries. By integrating human factors principles into safety management systems, Company can effectively identify and address the root causes of hand and finger injuries, leading to improved safety performance and reduced occupational hazards.
Smart Field Technology / Automation and New Technologies Applied to Projects
Authors: Sasipriya Rajendran
Published: October 2023

The smart field technology / Automation and New technologies used in Oil and gas projects attained specific attention from researchers because smart field technology and automation techniques improve the smart way of managing the plant process by communication between objects, machines, and people. The evolved technique of Cloud-based SCADA is one united system consisting of the things of the real world or combined process activities connected to the Internet via wired and wireless network structures. This structure of automation helps humans to manage plant operations in an effective and efficient manner by achieving required safety and improving plant processes with improved productivity. This paper has discussed the requirement of automation in the oil and gas industry and the benefit of Cloud-based SCADA using VPN in Oil and gas plants. The automated plant with Cloud-based SCADA using VPN can serve the function of a single node in a global setup. However, an automated oil and gas plant should be a forerunner of Cloud-based SCADA using VPN, by which the structure/characteristics can provide a solution to a lot of research challenges of Cloud-based SCADA.
Inter-Well Similarity Analysis as a Key Pre-Processing Step in Stratigraphy Interpretation Workflows
Authors: Subbotina M. V. , Bukhanov N. V. , Tirikov E. M. , Michael N. A.
Published: October 2023

High-resolution and high-quality stratigraphic markers are fundamental to geological modelling and operations. Stratigraphic interpretation workflows, however, are time-consuming due to the processing of massive amounts of data collected from geological surveys. This paper presents highlights of the importance of inter-well similarity analysis on the preprocessing stage for stratigraphic interpretation. The proposed methodology involves the use of common time series similarity metrics for quantitative estimation of pairwise similarity between wells to select appropriate ones for further interpretation. Experiments were conducted on the data collected from Groningen Gas field and petroleum basin in the Middle East. The impact of data selection on the final results was estimated based on the prediction quality metrics and computational time between baseline models (XGBoost classifier and simple Feedforward Neural Network) trained on the whole set of wells and models trained only on selected wells. The integration of similarity-based wells selection step allowed us to improve computational time of models, while preserving or even outperforming prediction quality. With the results of our experiments, we demonstrated the importance of the usage of only representative data for stratigraphic predictions and custom-made models’ creation for the target well. The proposed workflow can be beneficial for traditional manual correlations as well.
Augmented Learning Parameter Advisor for Wellbore Domain Interpretations
Authors: Rekik Karim , Bouyghf Abdelkabir , Zened Olfa , Kontsedal Tanya
Published: October 2023

The Parameter Advisor introduces an AI-powered solution for automating the selection of optimal parameter values in wellbore data interpretation. The aim is to reduce effort and time required for accurate interpretations. The software leverages machine learning algorithms, a comprehensive knowledge base, and collaboration among experts to enhance the interpretation process.

The overall approach includes data gathering, quality control, and validation. Relevant data is collected and stored in a cloud storage system. The software applies statistical techniques and unsupervised learning algorithms to ensure accuracy and identify patterns in the data. Once the database is established, the software provides recommendations for future analyses based on past interpretations and expert knowledge.

The results of tests conducted in the GRONINGEN and CASABE fields showed 92% accuracy compared to manual interpretation. The execution time for a Shale Volume interpretation was reduced by 64%. Collaborative studies with AkerBP in the Valhall field demonstrated an interpretation time reduction of approximately 70%.

This study presents a novel approach in the petroleum industry by automating parameter initiation using machine learning and cloud computing. It improves the speed, accuracy, and efficiency of wellbore data interpretation. The software's ability to recommend optimal parameter values based on previous interpretations and expert knowledge contributes to better decision-making. The findings emphasize the effectiveness of machine learning in automating interpretation tasks and enabling non-experts to interpret data accurately.

In summary, the proposed software streamlines the wellbore data interpretation process, reduces errors, and saves time. It enhances collaboration among experts, captures expert knowledge, and improves decision-making. The solution adds valuable insights to the petroleum industry by showcasing the power of machine learning in interpretation tasks and demonstrating its potential for transforming the field.
Bringing Old Non-Integral Wells Back to Life. Latest Unconventional Maintenance Techniques to Improve Asset Reliability, Sustainability and Reduce Carbon Footprint
Authors: Basit P. Eng. Abdul
Published: October 2023

Tree Valves and wellhead cavities provide critical barriers to control the flow of oil and gas from wellbore to the production system. To guarantee secure and Integral operation of wells in accordance with HSE requirements, implementation of an effective maintenance strategy is essential. Conventional methods might not always be successful in tackling various challenges faced in preserving and reestablishing the integrity of critical barriers at the Tree and wellhead. The industry has therefore resorted to cutting-edge unorthodox procedures in order to revive and maintain the best performance of these wells.

Three novel ways of maintenance that have yielded promising outcomes are:
An Experimental Study of Shale-Drilling Fluid Interactions to Enhance Long-Term Shale Inhibition
Authors: AlArfaj Mohammad , AlRashead Mohammad , AlOtaibi Mohammad
Published: October 2023

The objective of this paper is to discuss shale-fluid interactions while drilling clay-rich shale formations. Shale swelling, dispersion and sloughing are frequently encountered problems that may lead to excessive non-productive time. Comprehensive understanding of the behavior of the shale sample helps in mitigating shale drilling problems.

To understand the behavior of a shale sample when exposed to fluids, different types of characterization tests have been conducted starting with mineralogy identification using X-ray diffraction, X-ray fluorescence and also the conventional swelling tests. It went further to study the organic carbon content using thermogravimetric analysis.

After carrying out X-ray diffraction and thermogravimetric analyses on two shale samples to determine clay content and organic carbon content, a series of swelling tests were conducted using different types of non-inhibitive and inhibitive fluids. Linear shale swelling results are represented as the percentage of volume expansion as a function of swelling time while shale. Results showed high variance among shale inhibitors in terms of the ability to inhibit shale particles for prolonged time intervals

This paper provides a review of various factors that influence shale-drilling fluid interactions Due to the presence of some unique conditions, such as loss circulation, sometimes there is a need to use a low cost non-inhibitive mud instead of a high cost inhibitive mud to drill the next formation after the shale section. If the durability of original inhibition is not long enough to ensure smooth drilling of the the second zone, the previously drilled inhibited shale formation starts reacting again with the non-inhibitive drilling fluid. Hence, the reactive shale zone needs to be isolated by a casing string before drilling ahead. However, if inhibitive muds with long-term inhibition potential such as 24 hours or more are available, multiple formations including the shale formation can be drilled and cased in one hole section.
Lithostratigraphy Modeling with Transformer-Based Deep Learning and Natural Language Processing Techniques
Authors: Jacinto M. V. G. , Silva M. A. , de Oliveira L. H. L. , Medeiros D. R. , de Medeiros G. C. , Rodrigues T. C. , de Montalvão L. C. , de Almeida R. V.
Published: October 2023

In the Geosciences, sequence modeling algorithms such as long short-term memory - LSTM - have been widely used in various natural domains, like in earthquake and rainfall forecasting. A new architecture called Transformer has been used by the recent state-of-the-art models, capable of outperforming classical methods. Using this technology, this paper brings a modern approach that applies Transformers-based algorithms to solve a lithostratigraphy prediction challenge. We propose modifications to the original technique in order to embed geological information. In this sense, the lithological sequence is encoded as a sequence of integers and mapped by an embedding layer into a richer representation (a numerical vector per lithological element). We also incorporate the relative position of the lithological samples by adapting the original encoding. The database comprises four different wells located in the same onshore basin: two were used for training, one for validation and the last for test. They were filtered to guarantee that all wells’ data would have the same depth range. We ran 38 experiments with varying hyperparameters (number of transformer blocks, embedding size, learning rate and parallel attention heads). It was found that higher values of those variables indicate a higher model’s performance (in terms of F1-Score, Accuracy, Precision and Recall). The results achieved have accuracy and F1-Score concentrated between 0.89 and 0.92, showing consistency and good generalization capacity. Visually, we observe the model can approximate the lithostratigraphy within the geological wells’ context. Moreover, we developed 2 metrics in order to assess the model’s ability to detect lithological transitions, named ‘Transition Accuracy’ and ‘Expanded Transition Accuracy’. It was applied in 10 randomly selected intervals in the test data. Statistically, the ‘Expanded Transition Accuracy’ shows the model misses the transition in 0.3048 meters at most. Finally, we consider this paper presents a proof of concept in the use of transformer-based technologies for the modeling and prediction of lithostratigraphic sequences, a successful adaptation of NLP techniques to solve a geoscientific challenge.
Influence of Nanoparticle-Based Drilling Fluids on Egyptian Shale Swelling – An Experimental Investigation
Authors: Mady Ahmed , Abdelhafiz Mostafa M. , Dahab Abdel Sattar , Hegazy Gehad M. , Mahmoud Omar
Published: October 2023

Shale swelling is a major challenge in drilling operations and can lead to severe problems. The use of shale swelling inhibitors and plugging additives in drilling mud is a common solution to mitigate these problems. This study aims at investigating the influence of aluminum oxide (Al2O3) and copper oxide (CuO) nanoparticles (NPs) as mud additives on Egyptian shale swelling. Two formations were studied, Duwi and Dakhla shales. X-ray diffraction (XRD) was utilized to reveal their clay mineralogy. Low-solid non-dispersed mud (LSNDM) was tested as the base fluid. LSNDM is a common choice to drill an intermediate section which may contain minor strikes of shaley and problematic formations. Al2O3-NPs (15 nm) and CuO-NPs (40 nm) were added at different concentrations (0.1, 0.3, 0.5, 0.7, and 1.0 wt%) and the rheological and filtration properties were tested using standard viscometer and API filter press. Oedometer tester was used to examine the effect of NPs-based LSNDM on shale swelling. Adding NPs to the LSNDM yielded better rheological behavior. An increase in the yield point (YP) was obtained when using 0.3 and 0.5 wt% of Al2O3-NPs and 0.1, 0.3 and 1.0 wt% of CuO-NPs, which implies less solids sagging, higher cuttings carrying capacity and thus, more efficient hole-cleaning. Furthermore, the gel strength (GS) of the LSNDM showed improvements when adding NPs compared to the base. Also, the Herschel-Bulkley model was found to best fit the rheological behavior of the NPs-based LSNDM. Moreover, using both NPs yielded better filtration properties, especially at 0.3 wt% of Al2O3-NPs and 1 wt% CuO-NPs. The scanning electron microscopy (SEM) images showed better surface morphology and less porous microstructure of the filter cakes containing 0.3 wt% of Al2O3-NPs and CuO-NPs compared to the base fluid. Also, a decrease in the slope of the filtrate volume curve after 30 minutes (up to 90 minutes) was observed compared to the base fluid, which confirms better NPs dispersion. The composition of the clay minerals ranged from 80% montmorillonite to low kaolinite content (19%) for Dakhla shales, and from 48% montmorillonite to low kaolinite content (22%) for Duwi shales as revealed by XRD, which suggests high swelling ability, especially for Dakhla shales. The Oedometer swelling measurements showed a reduction in Duwi shale swelling by 30 and 35% when using 0.3 wt% of Al2O3-NPs and CuO-NPs, respectively, compared to the base. In addition, a significant reduction in the swelling of Dakhla shale was obtained when using 0.3 wt% of Al2O3-NPs and CuO-NPs by 52 and 63%, respectively. This paper discusses a new application concerning the efficient use of NPs in the drilling fluid industry, which may promote using NPs-based mud to drill the Egyptian oil and gas wells.
13 Years of Comprehensive Reservoir Monitoring and Surveillance in South of Oman EOR Polymer Project
Authors: Al Sawafi M. S. , Al Ghailani T. A. , Al Kindi O. , Al Abri M. , Al Hinai S.
Published: October 2023

The largest polymer flood field in the Middle East is operating since 2010 with more than 45 polymer patterns and around 300 wells. Throughout the last 12 years, the project went through several challenges such as injection uptime sustainability, polymer viscosity stability & polymer pumps uptime which have direct consequences on the field polymer oil recovery.

Running such a complex project requires detailed insight of all the dynamics of fluid movement. To understand the reservoir’s behavior, a thorough surveillance plan is carried out each year enabling the team to review the performance and take proper actions accordingly. There has been a significant amount of data collected to monitor the polymer performance from both surface and subsurface aspects such as wells production data, polymer and water injection rates, polymer viscosities, back produced polymer concentration, static and dynamic pressure, injection tubing head pressure, polymer daily consumptions and many others.

The big flood of data requires to be analyzed periodically. To achieve this, the team has come up with a set of automated tools to aid in visualizing the performance of the polymer flood at any point of time. The tools have pre-set logics that pull surveillance data and produces them as readable charts and tables, which helps the team to focus the efforts to the areas of concern.

In addition, the follow up on wells and patterns is run through a logic called exceptional based surveillance (EBS), where alarms are triggered when something falls out of the pre-set operating envelope.

All of the above has led to realizing more recovery from the polymer flood. The surveillance activities have demonstrated clearly how the data-driven and focused efforts lead to better performance. Alongside operational excellence, an oil gain of 50% was achieved in the last 3 years.

The paper will showcase examples of the data acquired, analyses done, and actions implemented to achieve oil gain.
Autonomous Parametric and Machine Learning Estimation Models Revolutionize Capital Expenditure Process of Engineering and Construction Project in Pttep
Authors: Srivaranon Suchart
Published: October 2023

Brownfield Cost Excellence is one of PTTEP digital transformation project; to resolve pain points of project cost estimation process for engineers. This project contains 2 development phases, 1st Phase (in 2022) is to provide the calculative features of cost estimator using autonomous parametrical techniques modelled by cost estimation relationships (CERs) of data with dependent variables by plotting the relationships of data on TIBCO Spotfire and using Natural Language Processing (NLP) such as IronPython and R-script to build up calculative features and shaping up Parametric equations of Major Equipment and key bulk material cost curves with prediction of residual.

In 2023, 2nd Phase project is focusing on development of cost estimation web-based application which have 6 modules to deliver Major Equipment cost, Bulk Material cost, Construction cost, Tariff cost, Allocation cost including Contingency cost. The calculative of Tariff cost (PTTEP engineer man-hour) is delivered by Machine Learning algorithms which developed by Multiple Regression Equations by joining of 3 different data sources (PTTEP TimeWrite, Project Tracking Information and historical transactions of project expense from SAP). The calculative of allocation cost (builds from historical transaction data of Helicopter/Vessel transportation gathering from legacy logistic system of PTTEP) is also delivered by Machine Learning algorithm developed by connecting of several data sources as similar as the development of Tariff algorithm. Lastly, the contingency cost is aiming to be developed further by collecting gaps between estimated project cost versus actual spending cost of project to diversify its distribution prior to statistically construct contingency algorithm based on each type of project. Ending results given from the cost estimation web-based application are readiness for budget preparation of engineering and construction project which is compiled to AACE estimate class III (Typical purpose of Brownfield cost excellence project is for preparing budget of engineering and construction project in the accuracy range of +30%/-20% in a proper manner).
A Data-Driven Approach for Forecasting Static Bottom Hole Pressure Using Machine Learning Methods
Authors: Alasiri S. , Ureiga S. , Madan M.
Published: October 2023

This research is proposing a new machine-learning approach for predicting static bottom hole pressure (SBHP) using localized injection and production ratio (IPR) data and related parameters. The methodology identifies key features, compares several machine learning techniques, and demonstrates the potential of the suggested approach to improve the precision and effectiveness of SBHP estimate using the available dataset. The goal is to offer an innovative methodology that can help with better SBHP forecasting using the data already available.

The machine learning approach involves a comprehensive methodology for forecasting SBHP in individual wells. Key parameters considered in this study include well depth, reservoir and wellbore properties, production and injection rates, time, and various flow and pressure data.

This research incorporated various types of features, including static, dynamic, numerical, and categorical data. To ensure optimal performance of the machine learning model, the data underwent preprocessing. The methodology employed decision trees, random forests, and gradient boosting as machine learning algorithms. The evaluation of the model involved the utilization of the coefficient of determination (R-squared), mean squared error (MSE), and root mean squared error (RMSE). Training the model relied on a dataset comprising more than 13,000 data points gathered from diverse fields and wells.

The results of this study indicate the precision of machine learning algorithms in forecasting static bottom hole pressure (SBHP) using individual well injection/production ratio and other essential parameters. By combining static, dynamic, numerical, and categorical the proposed approach achieved a remarkable 97% accuracy in SBHP prediction. Decision trees, random forests, and neural networks, which were the machine learning algorithms employed in this research, demonstrated encouraging outcomes in SBHP forecasting. Notably, gradient boosting regressor algorithm exhibited superior performance, delivering highly accurate SBHP predictions.
Integrated Solution using Coriolis Flowmeter, Borehole Image, and Deep Shear Wave Imaging to Cross-Validate Open Fractures in Tight Carbonate Reservoirs
Authors: Nguyen Kim Long , Fahmy Mahmoud Fawzy , Al-Mutairi Nami , Al-Morakhi Rasha , Dasma Mohammed , Al-Mijrin Lulwa , Quttainah Riyad , Bouchou Ramdane , Kerrouche Nawal , Chenglong Wang , Ousididene Karim , El-Masry Mohab
Published: October 2023

Wellbore natural fracture systems characterization is a key element to achieve the successful exploitation of tight reservoirs, where one of the dominant aspects is permeability. Carbonate reservoir fracture evaluation is challenging in terms of type, density, aperture, and extension. Traditionally, fractured reservoirs are evaluated based on Borehole imaging (BHI) data. However, the borehole drilled with oil-based mud (OBM) poses constraints in the image quality of the logs. The oil-based mud has higher resistivity than the conductive mud that limits the depth of investigation and reduces the ability to identify surface features and formations. Ultrasonic imaging tools provide higher resolution than OBM imaging tools due to its capability to read deeper into the formation. However, the ultrasonic imaging tools are specialized equipment with high operational cost and the quality of measurement is affected due to noises from drilling or other sources. Moreover, the meaning of features observed from image logs is a matter of interpretation at the borehole wall only. This introduces a degree of uncertainty, which might be greatly reduced by integrating other measurements such as Deep Shear Wave Imaging (DSWI) & Coriolis micro mud-loss detection data. The first is to map the near borehole environment for fractures as well as their extension far away from the borehole (within ~150 ft vicinity), while the latter provides information on the presence of open fractures while drilling by detecting minor well kicks and mud-losses. Therefore, integration of the multiple sources of information significantly enhances the benefits of all.

This paper aim to present an integrated workflow, which combines above-mentioned methods to enhance the confidence of fracture interpretation at & away from the wellbore, resulting in a detailed description of the natural fracture system in a deviated well drilled through a tight carbonate reservoir in Kra Al-Maru (KM) field recently that supports the development strategy.
Getting Practical About the Future of Work a Framework for Upskilling and Reskilling Based on Trends Impacting the Oil and Gas Industry
Authors: Saad Amal , Zohair Alifiya
Published: October 2023

The FoW is a forecast of how work, workers, and workplaces will evolve in the upcoming years (SHRM, 2022). According to McKinsey, by 2030, up to 30 to 40 percent of all workers in developed countries may need to move to new occupations or upgrade their skills significantly with the impact of Digitalization and Artificial Intelligence AI (Hancock, B., et. Al. 2020). For some organizations, the future of work is exciting. For most, it creates anxiety. Studies indicate variation in readiness for the future based on country, gender (LHH, 2022), and even generation (Chambers, N., 2019). While many organizations know that re-skilling and up-skilling will be required; most are not preparing for this yet (Hancock, B., et Al. 2020).

This research theorizes that the lack of action toward upskilling and reskilling may contribute to a lack of a usable framework. The study set out to identify the skills required for the future of work in the oil and gas industry. The study started with a comprehensive literature review to identify the key trends that would impact the global oil and gas industry in the short, long, and mid-term. Qualitative research was then conducted through focus groups with managers and executives from the industry to match each trend to skill requirements.

The results were compiled into a framework for organizations in Oil and Gas to plan for workforce upskilling and reskilling. The framework allows organization leadership to review their current workforce skills with the expected skills based on this future of work analysis and to create and implement programs that support upskilling and reskilling towards the future of work. This article makes recommendations for the implementation of such programs.

While the future of work is recognized as important by executives, there is a gap in practical frameworks like the one created by this research.
PDO Flare Management Transformation Towards NZE Journey
Authors: Al Waily N. A. , Al Riyami B. A. , Al Mahrooqi M. A.
Published: October 2023

The objective of this paper is to share insights of the best practices of Petroleum Development Oman (PDO's) in the energy sector as the leading organization in the country in the domain of flare management and transformation towards NZE journey. The scope of this paper shall entail both; soft initiatives and projects initiatives with examples of applications and post implementation with demonstrated results yielding tremendous step change improvements within the organization. Flare management is a key pillar in PDO's Decarbonization strategy, amounting to approximately 17% of its total GHG emissions (2nd largest contributor after power generation). Significant efforts were placed to reduce company's flaring emissions through an integrated manner approach and active emissions abatement programs consisting of cross-organizational projects, quick wins leveraging via process optimization and cultural change through awareness and training, strict waiver compliance and adherence to flaring limits. Management's role in driving changes was also vital to organizational and performance cultural change through monthly huddles and flaring accounting assurance via flare meters health checks reviews, proactive flare containment via Exception Based Surveillance and Gap to Potential Reliability Program for gas system reliability enhancement. Company's performance scorecard includes flaring as a key KPI and proportionally linked to total HSE performance. In 2022, PDO has reduced its gas flaring volumes by more than 46% compared to its 2019 levels. PDO also endorsed the Zero Routine Flaring (ZRF) initiative by the World Bank and hence flare recovery projects have been commissioned in addition to implementing new technology trials for small-scale volumes; an Energy Management System was developed which includes projects maturation funnel to ensure their timely and effective commissioning. A campaign was launched for passing valves to flare and repair plans and verification of sufficient combustion efficiency and flare measurements in accordance to Oil and Gas Methane Partnership (OGMP 2.0) pledge with the aim of achieving gold standard.
First Successful Hydraulic Fracture Treatment in Deep Pre-Khuff Clastics: Lessons Learned from the Implementation of Cutting-Edge Technology Offshore Abu Dhabi
Authors: Buijs H. , Ibrahim M. , Basioni Mahmoud , Alloghani J. A. , Elmahdi A. , Sayed S. , Swedan T. , Azoug Y. , Al Kaabi A. , Bulekbay A. , Efnik M. , Al Hammadi A. E. , Al Kindi R. K. , Al Aryani F. M. , Bregar U. B. , Nuriakhmetov R.
Published: October 2023

This paper describes the development and implementation of a multi-frac completion strategy that improved the well performance by more than 500% in the first successful hydraulic fracture treatment pumped in deep Pre-Khuff targets. The primary target reservoir is a deep tight gas sandstone that can be found at depths exciding 16,000 ft with temperatures above 350°F. Given the reservoir characteristics, primarily its low permeability, hydraulic fracturing is required to achieve commercial production rates. The design of the fracture stimulation treatment presented several challenging conditions including high formation temperature, closure stress, and fracture pressure with the potential for post treatment proppant flowback in an offshore environment.

The learning curve was collapsed by interdisciplinary evaluation of diverse data sets from legacy and newly drilled wells. Mechanistic models were employed in a grid-oriented planar 3D hydraulic fracture simulator to optimize treatments and to understand changes in production performance related to specific multi-fractured completion strategies, guiding the optimization process. This work led to the successful implementation nano-proppants, pseudo-stable fracturing fluids and, among other technologies, specialized perforation guns designed for low stress anisotropy conditions. This paper will provide insights on the results and lessons learned from the implementation of cutting-edge hydraulic fracturing technology that provided the means to overcome challenges to unlock hard to recover reserves.
Auto-Detecting Drilling Vibrations Through Intelligent 4IR Solution
Authors: Gowida Ahmed , Saadeldin Ramy , Gamal Hany , Elkatatny Salaheldin
Published: October 2023

Downhole vibrations have a significant impact on downhole equipment performance, wellbore stability, and drilling efficiency. High levels of drillstring vibration often led to equipment failure, hole problems, energy loss, and reduced drilling performance. Detecting these vibrations through downhole sensors is costly and time-consuming. However, advancements in new technologies and machine learning present opportunities for intelligent data analysis and addressing complex technical problems. In this study, a successful application of a machine learning technique was proposed to auto-detect downhole vibrations during the drilling curve section using surface drilling data. The axial, torsional, and lateral drillstring vibration modes were detected using random forests (RF) machine learning models trained with real field data. The model was developed through comprehensive data-driven research, including data collection, preprocessing, analytics, model optimization, and performance evaluation. Overall, the developed machine learning model achieved high accuracy, with R values exceeding 0.87 and average absolute percentage error (AAPE) below 8.4% between actual readings and predictions. The proposed ML algorithm offers an intelligent solution for predicting drilling vibrations using only surface drilling parameters, eliminating the need for downhole sensors. Implementing this solution on drilling rigs enables real-time monitoring of vibrations and supports automated advisory systems. It provides valuable insights for directional drillers and drilling engineers, facilitating drilling optimization, and improved well planning.
Enhancing Rigless Intervention Efficiency in Bi-Lateral Oil Producer Through Coiled Tubing Optical Telemetry, Multilateral Tool, and Single-Phase Retarded Inorganic Acid: A Case Study from South Kuwait
Authors: Al-Mutairi F. , Sanyal S. , Al-Mutawa M. , Al-Ayyaf E. , Al-Bader A. , Al-Otaibi A. , Al-Anezi T. , Al-Dhafiri S. J. , Al-Najim A. , Aldawaish M. , Al-Otaibi A. , Hedi Slama M. , Badrawy K. , Molero N. , Ujagar U. , Adel Sebaih M. , Ayyad H.
Published: October 2023

Developments in horizontal drilling and completion techniques have enabled drilling an increasing number of wells. In the pursuit of cost optimization and enhanced recovery through maximized reservoir contact, operators are placing special interest on multilateral completions. However, multilateral wells add complexity to rigless intervention and production enhancement work as those activities are heavily dependent on lateral accessibility.

An advanced rigless intervention in a multilateral well relied on coiled tubing (CT) equipped with optical fiber and real-time downhole measurements and a multilateral entry tool (MLT). Downhole readings included casing collar locator (CCL), gamma ray, CT internal pressure, annulus pressure, annulus temperature, and axial force. Optical telemetry was used for lateral identification and for optimal actuation of the orienting and bent sub-components of the MLT while profiling and accessing the lateral. Throughout the stimulation treatment, optical telemetry was instrumental to understanding changing downhole conditions and keeping injection rates below fracturing pressure.

The candidate well was a carbonate bi-lateral oil producer completed through 6 1/8-in. uncased horizontal sections. Both laterals exhibited average length of 2,400 ft, and the reservoir featured a bottomhole temperature near 140°F and bottomhole pressure close to 3,050 psi. Because of real-time optical telemetry, profiling of the lateral was completed in less than 3 hours, which represents 50% reduction of the average profiling time in similar wells. Both laterals were stimulated via CT with a total of 4,000 bbl of single-phase retarded inorganic acid system (SPRIAS), which enables carbonate stimulation with better wormholing performance than emulsified acid while eliminating its drawbacks, such as high CT friction pressure and potential emulsion issues. In comparison with emulsified acid, the pumping time with SPRIAS was 35% lower, bringing additional efficiency to the intervention. The ability to make fast, informed decisions based on optical telemetry was the cornerstone of this intervention approach.

This paper documents a value case study for CT rigless intervention in South Kuwait, where the combination of an array of technologies, such as CT optical telemetry, MLT, and SPRIAS, enhanced rigless intervention efficiency, reducing total operation time by 30%, when compared to conventional CT stimulation approach in multilateral wells.