bluuebunny/crossref_metadata_2025_split huggingface dataset:
High Resolution Acoustic Analysis for Improved Formation Evaluation of Carbonate and Clastic Reservoirs
Authors: Mahiout Said , Torlov Vladislav , Gouda Mohamed , Abouzaid Ahmed 
Published: October 2022

Abstract
Developing new interpretation methods in line with new technology measurements to improve reservoir characterization becomes a must to overcome the challenges that petrophysicists are facing on a daily basis, among others, thinly bedded reservoirs. The standard acoustic logs vertical resolution (VR) is oftentimes insufficient to resolve the Formation features. In the case of laminated thin beds (LTB), conventional petrophysical characterization has often delivered results, such as hydrocarbon saturation, affected by uncertainties. The main objective of this work is to develop a methodology, and to reliably integrate high-resolution acoustic processing outputs into the Formation evaluation (FE) routines, including the quality controls to identify and remove artifacts and ultimately enhance the accuracy of reservoir characterization.
The approach used in this study is based on the high-resolution acoustic slowness analysis. The measurement standard VR of the acoustic array measurement is determined by the array aperture (the length of the receiver’s array section; generally 3.5 ft), which tends to obscure features that are thinner than the aperture length. The configuration of the highest resolution is 0.5 ft aperture subarray combination, which considers only two receiver levels in the subarray, i.e., single Rx-Rx spacing. The processing has limitations, inherent to physics; one of them is the "Validity condition". The high-resolution acoustic slowness methodology cannot be applied to acoustic waveform arrivals with very long wavelengths. The subarray aperture should be greater than a quarter wavelength of the wave being used. In addition, as increasing the resolution goes hand in hand with decreasing the number of waveforms, the signal-to-noise ratio decreases. There are several quality control and consistency check workflows to be introduced in this study, which will help in differentiating between the real high-resolution acoustic slowness data and any encountered processing artifacts.
These analyses will enable the right evaluation strategy that will enhance the accuracy of FE. Examples highlighting the value of high-resolution acoustic processing/analysis for both carbonate and clastic reservoirs evaluation are provided in the paper. Finally, this approach will evaluate the potential added value to the FE of the heterogeneous reservoirs.
We propose workflows in various Formations and borehole settings to demonstrate that with the aid of enhanced VR acoustic data, the log interpretation becomes more representative of the actual subsurface. The resulting sonic profiles show better consistency with other log responses of similar resolution. These workflows then can be used on a more regular basis, especially in complex situations.
Application of an Integrated Geochemical Approach to Assess Hydrocarbon Presence and its Composition Below the Hitherto Free Water Level. A Case Study from Flank Appraisal Wells of a Producing Field in the Onshore UAE
Authors: Syofyan Syofvas , Ahsan Syed Asif , Koksalan Tamer , Bal Baheeth Asma Hassan Ali , Al-Shamsi Saif Ghanim , El Gazar Ashraf Lotfy , Alkhawaja Ahmed Adnan Ali , Chenji Wei 
Published: October 2022

AbstractRecent appraisal campaign on a producing field in onshore Abu Dhabi has found evidence of oil presence 60-170 ft below hitherto known Free Water Level (FWL). The Oil water contact (OWC) was identified by electrical and mud logs, geochemistry analyses on core plugs and well cuttings, core stain, core UV, thin sections and Dean Stark saturation. The calculated volume of the hydrocarbon (HC) below the FWL is significant. However, below FWL, one well tested traces of oil. A comprehensive study including geochemistry was commissioned to understand hydrocarbon composition and its mobility for reserves evaluation and future development planTotal of 1600 ft of core was acquired from three appraisal wells and cuttings from two wells. Pyrolysis was conducted to evaluate hydrocarbon presence and its composition. Subsequently, a subset of sample from pyrolysis data was selected for solvent extraction and detailed analysis for oil characterization. Saturates, Aromatics, Resins and Asphaltene (SARA) fractions were measured on few samples. Finally, Gas Chromatography and Compound Specific Isotope Analysis (CSIA) were performed to compare the extract with the produced oil from the crestal area to assess the variation of HC compositionParameters derived from mud log have provided a good understanding of hydrocarbon distribution. Thin section and Scan Electron Microscope (SEM) observation showed presence of black, solid carbon particles (Solid bitumen) in the pore spaces despite solvent cleaning. Oil Saturation Index (OSI) and solvent extraction show lower and higher values below and above the OWC, respectively. S1 and S2 yields provided a detailed understanding of hydrocarbon distribution. Saturate composition was highest at reservoir top where the only MDT sample (22% of oil) was successfully collected, whereas it was low where only water on MDT and traces of oil (&gt;99% BSW) were collected during testing.The pyrolysis data, typically applied to assess source rock quality and maturity, has been utilized successfully in a novel approach to assess the oil distribution and its composition in a conventional reservoir. It is now understood that, in addition to reservoir rock facies, oil facies should also be established to achieve a comprehensive reservoir assessment in this challenging reservoir that has witnessed multiple phases of hydrocarbon charge as indicated by solid bitumen and liquid hydrocarbon. These dispersed bitumen particles are likely to provide large adsorption surface areas for any oil that might otherwise would have been available for flow upon testing.
Gys-Recon a Field Reconnaissance Engine Based on a Combination of Gis, Machine Learning, and Computer Vision Technologies on Satellite Images and Maps
Authors: Elhallaoui Yassine 
Published: October 2022

AbstractIn Field Operations, area information gathering is a powerful step that identifies and locate every Geo-spatial feature present in that area of interest which can provide valuable data that trigger other processes such as operational risks, hazards, and cost analysis and review, one of the usual and common ways to do this information gathering is using vehicles and drive around the area of interest and locate, report what human eye can see to be shared with other involved parties in a report with coordinates, photos, and remarks in overall that gives hints of how the area looks like and the decision will be based on gut feelings and experiences.The Geographical y-hat system for reconnaissance (gŷs-recon), is a solution not only to identify the presence of features using satellite images and maps in the area of interest but to simulate the real- world operational scenarios whatever is the type of operations using 2D and 3D interfaces, giving statistical figures for every feature such as (Urban areas, Sand dunes, Forests, flat terrain and many more…), that cover the occupation in square meters, the curvature of the terrain, contours and all the necessary Geo-spatial analysis, with the help of unsupervised machine learning algorithms also computer vision, combining those technologies with the GIS analysis, it generates insightful data in number-based forms that can be used in plots, 3D visualizations and creating accurate risks coefficients and percentages.The system is compatible with most of the GIS input format including the Geo-Tiff that contain the satellite images, the Elevation model data, and the photogrammetry data.The system results were satisfactory in several ARGAS projects and lead to reassessing some risks and operational calculations.
First 3D Reservoir Model of a Giant Brown-Field (Around 1000 Wells) Built with Zero Pseudo-Wells
Authors: Pamungkas Setiyo , Aidarbayev Sanat , Al-Hammadi Fatema Yousef Ali 
Published: October 2022

Abstract
Middle East brown fields are penetrated by, more or less, around 1000 wells with long-production history. Attempts to incorporate all these wells create huge challenge caused by uncertainty related to well data discrepancy. The discrepancy in acquisition methods, tools, vintages are few factors to name that are root causes resulting in different depth values. Therefore, varying depth makes difficult to build structure of 3D reservoir model without non-geological anomalies such as distorted, collapsed, non-orthogonal cells.
Methodology is to introduce uncertainty to all data feeding the structure modelling process. Following data is used with their ranges of uncertainties: interval velocity, seismic time maps, thickness maps, geological markers and well surveys. These ranges have to be identified quantitatively as they will give us flexibility to integrate data in the model within justifiable windows. Initially, the maps are allowed to change and try to integrate all data without well survey modifications. If, even after number of iterations, data is still not consistent resulting non-geological anomalies, then it is good to try allowing survey to change, but cautiously.
Following application of this workflow, the data started to come in agreement and resulted in smooth, geologically reasonable subsurface structures. Horizontal wells targeting multiple thin carbonates are the most challenging to place them correctly. These wells require a lot of iterations or manual intervention to incorporate in the model, sometimes by adding number of pseudo-wells. Figure 1 shows even those examples can result in geological markers, of both vertical and deviated sections, match structure surfaces where horizontal trajectory at right penetrated layers. Worth to mention that the integration of almost ~1000 wells required zero pseudo-wells that helps to avoid introducing unrealistic noise to the data.
Successful implementation of this project made this giant field one of the first brownfields that incorporated all data in consistent manner without using pseudo-wells. This structure model will maximize the value from ADNOC's existing data resources to reduce uncertainties during subsequent property and dynamic modelling stages plus while drilling future wells. Average estimates show that proper integration of all data can bring minimum $18 million in value.
Thermal Analysis of the Wellbore System During Drilling Operations Using an Improved Drilling Simulator
Authors: Abdelhafiz Mostafa Magdy , Oppelt Joachim , Hegele Luiz Adolfo 
Published: October 2022

Abstract
Prediction of the wellbore fluids and formation temperatures during well construction is an essential step for efficient drilling operation. This work presents the applications of an improved comprehensive drilling simulator for predicting the wellbore system temperature during the drilling process. A fully transient numerical model of the wellbore temperature is developed for drilling and geothermal production applications. The model describes the dynamic behaviour of the thermal state of the wellbore during circulation, drilling and static conditions. The developed model is successfully implemented with the commercial virtual drilling simulator through an application programming interface (API) provided by the simulator. The model is capable to predict the temperature of the wellbore fluids, drill string, casing, cement, and rock formation with variable properties in real-time. This implementation allows the coupling of the thermal model with other physical models, which leads to more advanced and realistic simulations. The model is assessed and validated through a direct comparison with field data. The data is acquired during the drilling of a geothermal well located in the Hanover area in Germany. The results showed a good agreement between the predicted outlet fluid temperature and the measured one. Furthermore, an analysis of the effect of various parameters on the wellbore system temperature is performed. These parameters include drilling parameters such as weight on bit, bit and drill string rpm, circulation rate and drilling rate, in addition to the wellbore geometry parameters such as wellbore radius, casing and drill string sizes. This analysis showed the impact of these parameters on the wellbore temperature profile including the critical areas such as the casing setting point and bottom hole assembly. This information may lead to enhancing the wellbore stability by monitoring the thermal stresses, especially in high-temperature wells. Moreover, predicting the drill bit temperature can result in increasing the lifetime of the bit by adjusting the operating conditions to keep the bit temperature within the specified range. Based on these results, the enhanced drilling simulator with the transient temperature model showed to be a suitable tool for effective well planning.
Methodological Engineering Approach in Designing Injector and Observation Wells Incorporating MMV Requirements in Carbonate CCS Project in Offshore Malaysia
Authors: Balakrisnan Muralithran , Bt A Halim Rahimah , Johan Ahmad Luqman , Ahmad Ani M Qayyum , Ismail Ahmad Fadhli , Yap Yun Thiam , Abd Hakim Hazlan , Abu Bakar M Aizat , Bt M Safari Lai Harni Farihah 
Published: October 2022

Abstract
We will be showcasing the practical approaches / considerations in designing a CO2 injection well covering the engineering aspects, cost optimization and sustainable design for the injection period. This will also include the software aided simulations for the practical load analysis for selection design parameters and materials for the CO2 injector in an optimized manner. We will also address the importance and relevance of the MMV (monitoring, measurement, and verification) method to the CCS project and its influence on the well engineering scope.
To deliver a sustainable well design, we referenced the existing CCS project against those undertaken in other parts of the world and identified the challenges, best practices, and operational approaches undertaken in those other wells. The well design and engineering has taken into consideration the existing internal and industry standards as well as lessons learnt from other CCS campaigns. The evaluation for the well design and construction was conducted to identify potential gaps lacking in the CO2 injection design in offshore carbonate environment. From the identified injection location, evaluation criteria were finalized to assist in decision making for the overall well design requirement covering injectors and observations wells.
The injector well design and construction will be able to successfully counter the subsurface challenges faced in the overpressure clastic formation and depleted carbonate reservoir. The design of the well will also be capable of surviving the external and internal loads due to the injection operation for the duration of the campaign life. Furthermore, the design has taken into consideration the temperature reversal impact on surface and downhole due the supercritical CO2 fluids which has low temperature in nature. We also developed testing procedures and evaluation matrix for the fluid wetted (CO2 injection fluid) sections to optimize material selection for the wetted zone as a major well cost contributor resulting from the CO2 resistance materials. We are converting some of the identified end of field life producer wells into observations wells via sidetracking and workover methods. The current overall well placement between the injectors and observation wells has achieved a comprehensive coverage area for monitoring of the CO2 plume migration across the carbonate field.
This is the first big scale campaign covering the offshore depleted carbonate field as a CCS candidate in the world as previous practices were only undertaken in the clastic environment. Success of this campaign opens more opportunities for achieving net zero carbon emission in the oil and gas industry.
Continuous Dynamic Drill-Off Test Whilst Drilling Using Reinforcement Learning in Autonomous Rotary Drilling System
Authors: Amadi Kingsley Williams , Iyalla Ibiye , Radhakrishna Prabhua , Al Saba Mortadha Torki , Waly Marwa Mustapha 
Published: October 2022

Abstract
In the development of autonomous downhole drilling systems, decision-making in the selection of optimized operating parameters has become one of the technical difficulties. Traditionally, the driller performs a trial-and-error approach in search of optimal parameters, which is now less effective and non-sustainable to the changing drilling environment. This paper presents a decision-making process using reinforcement Q-learning algorithms that can act as real-time optimization algorithms for selecting an optimal operating parameter for rotary drilling systems using Q-learning on experimental published data from the literature.
The reinforcement learning framework is a stochastic approximate dynamic programming, with varying estimation techniques for goal-directed sequential learning from interaction paradigms. First, a Markov Decision Process (MDP) is established by analyzing agent exploration and exploitation of possible actions taken in an environment. Second, the state set and action set are designed by the synthesized consideration of surface operating parameters from the published data within the range of operational limit. Then, sequentially, at each timestep, the agent takes an action (e.g., changing rotary speed or changing axial force) that makes the environment (formation) transition from one state to another. Consequently, the agent receives a reward (e.g., distance drilled) before taking the next action. Furthermore, a recursive reinforcement Q-learning algorithm is developed mainly based on the reward function and update function.
Analysis of experimental data on drilling was implemented for five states of axial force parameters with five feed rate decisions on each of the states, whilst having distance of a hole drilled as a reward. The proposed optimization model computed using value iteration showed that following Decision 2 yielded the best result. The analysis results also revealed that the optimal value function was reached irrespective of the initial state conditions. The agent's objective is to learn policy mapping from states to actions such that the agent's cumulative reward (footage drilled) is maximized.
The result of this research could be used as a decision-making tool in drilling operations that provides an engineered approach for optimal operating parameter selection and improvement in the efficiency of the drilling process in terms of cost and time.
Significant Stuck Pipe Event Reduction Realized Through Structured Holistic Approach Machine Learning and Artificial Intelligence
Authors: Salehi Yasir , Ziada Attia , Rawahi Zahran 
Published: October 2022

Abstract
Wellbore stability problems, such as stuck pipe and tight spots, are one of the most critical risks that affect drilling operations. Over several years, Oil and Gas Operators in the Middle East have been facing problems associated with stuck pipe and tight spot events, which have a major impact on drilling efficiency, well cost, and the carbon footprint of drilling operations. On average, the operator loses around 200 days per year in terms of Non-Productive Time(NPT) on stuck events and associated fishing operations.
Wellbore stability problems are hard to predict due to the varying conditions of drilling operations: different lithologies, drilling parameters, pressures, equipment, shifting crews, and multiple well designs. All these factors make the occurrence of stuck events quite hard to mitigate when relying on human intervention only.
In Petroleum Development Oman (PDO), we lost, between 2013 and 2017, a total of 1,568 days due to stuck events. This equated to a cost of US$15.5 million/year on average, which included the cost of tools lost in hole. In 2018 PDO's Management formed an interdisciplinary taskforce with the objective of analysing stuck events to find solutions which would reduce the cost of stuck events by 50% per year.
At the same time PDO decided to develop an Artificial Intelligence (AI) driven tool that leverages the whole breadth and depth of data (reports, sensor data, well engineering data, lithology data, etc.) available to predict and prevent wellbore stability problems. The tool, known as the "Stuck Pipe & Tight-Spot Event Prediction" (STEP) tool, informs well engineers and rig crews about possible risks, both during the well planning and well execution phase, suggesting possible mitigation measures to avoid getting stuck.
The taskforce's hard and diligent work, along with the use of the STEP tool, resulted in a significant reduction in stuck events, associated time, cost, HSE exposure and production deferment.
Machine Learning Delivers Automated Feedback on Real Time Key Performance Indicators During Drilling Operations
Authors: Krikor Ara , Khambete Shreepad Purushottam , Bimastianto Paulinus Abhyudaya , Cotten Michael Bradley , Toader Lucian , Landaeta Rivas Fernando Jose , Duivala Shahid Yakubbhai , Mughal Muhammad Idrees , Al Ameri Suhail Mohammed , Couzigou Erwan , Al-Marzouqi Adel A/Rahman , Pausin Wiliem , Vallet Laurent François , Chevallier Bertrand , Vaslin Antoine , Ullah Nadeem Hidayat , Qadir Ahsan , Al Khufash Hassan Walid , Shareef Raneef Mohamed , Ul Islam Muhammad Ashar 
Published: October 2022

Abstract
Drilling, tripping and running casing represents approximately fifty percent of the total well time, where the connection time KPI is the common performance indicator for those operations. Therefore, enabling real-time monitoring on drilling weight to weight and tripping connection time KPI's will add significant value through well time saving. The objective of this paper is to discuss the detailed implementation of machine learning to automate the detection and computation of the KPI's in real-time.
The existing method for drilling performance monitoring requires extensive human data interpretation to calibrate the parameters required in this process. To overcome the complexity and reduce the human interaction, the automated Rig state and Drill state activity level were implemented based on Machine Learning (ML). The algorithm learns from the previous connections, drilling stand or tripping conditions to define the thresholds necessary to determine the current rig operation. With automatic rig activity detection, statistics to monitor the performance can be done in a systematic way. As a result, consistency of computation allows to compare performance and to improve it.
The automated process using Machine Learning (ML) delivered consistent and powerful real time KPI computation, this helped to eliminate any human interpretation. This enabled real-time performance analysis delivery to rig site operations team. The machine learning model results were compared with the existing performance engine output and the comparison showed accurate and identical rig state/drill state detection and KPI's computation.
The initial potential time saving with the implementation of this methodology is estimated around 15%, this was achieved through performance improvement on drilling and tripping connection KPI's. Further potential time saving can be achieved by extending the concept to track casing and liner running performance monitoring and other relevant drilling activities.
This project introduces novel Rig state detection and KPI computation based on automated machine leaning model, demonstrating the benefits through improvement in drilling performance. The approach allows operators to mitigate data issues related with human interpretation and demonstrate real-time, high frequency and high-accuracy KPI's to significantly improve the drilling performance.