bluuebunny/crossref_metadata_2025_split huggingface dataset:
Data Driven Well Production Forecast for Improved Reservoir Management and FDP Optimization - A Case Study from a Super Giant Reservoir in Abu Dhabi
Authors: Belayouni N. , Gerges N. , Franco A. , Shaik I. , Ayoubi R. , ramatullayev S. , Gore S. 
Published: November 2024

Abstract
This paper aims to present an integrated approach for well forecasting that combines advanced Machine Learning (ML) techniques to provide accurate oil, gas and water predictions for existing and new wells in oil reservoirs.
Well forecasting is a critical task in the oil and gas industry for optimizing production and maximizing recovery from reservoirs. Traditionally, decline curve analysis has been widely used for well forecasting, but it has limitations in complex reservoirs or wells with uncertain decline trends. The proposed approach overcomes these limitations by utilizing advanced Machine Learning techniques that leverage complex real data for improved production forecasting. By incorporating ML algorithms, the proposed approach can provide more accurate and reliable predictions, particularly in challenging reservoir conditions where traditional methods may fall short.
The proposed approach for well forecasting involves several key steps. Firstly, historical data of wells, including production, choke, well head flowing pressure, producing days, injection data, well connection properties, and 3D properties, are collected and preprocessed. Next, 2D properties, such as well location, spatial coordinates, and spatial attributes, are extracted from the 3D properties of the reservoir using geospatial algorithms. Then, for existing wells an ensemble of ML supervised algorithm is trained well by well with enough historical data, and nearby wells are used to train non mature wells. For future new wells, historical data plus geological 2D properties is used to create a model per reservoir. This involves collecting and preprocessing data from nearby wells in the vicinity of the new drilling location to obtain relevant properties. Uncertainty intervals are calculated using quantiles to quantify the uncertainty in the predictions.
The integrated well forecasting approach presented in this paper provides a comprehensive solution for predicting well production in oil reservoirs. We used 2 different approaches to predict oil, gas and water production depending on the availability of historical data. Our approach allowed accurate predictions in complex reservoirs where wells have no clear decline trends. The use of uncertainty intervals provides a measure of prediction uncertainty, allowing for informed decision-making.
Stability Sensitivity Analysis of 100,000 Ton FPSO
Authors: Yu Boqian , Ling Aijun , Huo Guojing , Li Linbing , Li Hongtao , Liang Yuanhua 
Published: November 2024

Abstract
In this paper, a certain type of FPSO as an example, the relationship between the ship stability criteria and the change of the upper block in the preliminary design process of FPSO is proposed, and the basic method to ensure the stability of the FPSO is obtained. Based on the ship type and general layout of a FPSO, the problem of leaving enough ship stability criteria for the change of the upper block design scheme in the early design is studied. Through the calculation of stability and freeboard by MOSES, a large number of data are extracted and statistically analyzed by batch processing, and the amplitude of the change of FPSO caused by the change of the block is determined. Through the use of data processing and statistical methods, the limit of the stability change caused by the change of the block of FPSO is determined, providing quantitative ship stability criteria design method and stability sensitivity analysis method for the preliminary design of FPSO.
Breaking New Records of WRFM Gap to Potential in Artificially lifted Heavy Oil Reservoir in Petroleum Development Oman (PDO)
Authors: AL Riyami Samiya , AL Zahli Hamood , Hajri Hamood , Jabri Reham , Hinai Suliman 
Published: November 2024

Abstract
Well, Reservoir and Facility Management (WRFM) strategy provide a framework to manage and optimize wells, and reservoirs performance in any petroleum asset. WRFM aim to maximize hydrocarbon recovery, by ensuring the realization of the developed reserves, and optimizing short-term production level within provided budget. The scope of WRFM includes well integrity, surveillance, restoration, and optimization (CP-238 WRFM et al. 2022).
Gap to Potential (GtP) are one of the main WRFM excellence process that aim to reduce the gap between field production and potential. GtP exercise is meant to optimize the overall production system continuously (Al Balushi et al.2023). It has six main pillars which are deferment, water management, well optimization, fail Less, greenhouse gases (GHG) and energy saving. Execution of more optimizations are one of the main pillars that arrest field production decline, reduce frequent failure, and increase field production.
Field M is a matured heavy oil asset located in South Oman. In 2023 GtP optimization was one of the focus areas in field M well and reservoir management. The target was to boost all optimization activities with aspiration to uplift optimization contribution to total field production. This paper illustrates the work that has been implemented to advance optimization execution and demonstrate the resulted gain which raise field production dramatically and showing clear oil gain above the baseline. The execution of more optimizations includes proactive pump replacement, pump upsize, implementation of new technologies (Hydraulic frac, AICD, ESPCP and others), well clean-out and stimulation resulted in increase of optimization contribution to field M production from 4.75% in 2022 to 7.32 % by end of 2023. Field M GtP optimization activities breaking a new record in the GtP across PDO and generated around 3% increase in cash revenue.
Applying Machine Learning Methods to Identify Reservoir Features That Highly Affect Water Encroachment Based on Transforming Unsupervised to Supervised Model
Authors: Carlos Rodriguez , Ethar Al Naser , Mari Alqahtani , Osamah almansour 
Published: November 2024

Abstract
Machine learning methods are increasingly applied to identify reservoir features that significantly impact water encroachment in heterogeneous oil reservoirs. This study applies machine learning to identify reservoir characteristics most responsible for rapid water encroachment, leading to an increasing water cut trend. In heterogeneous and naturally fractured reservoir, multiple sets of features contribute to higher water cut trends, governed by the areal and depth variations in rock and fluid properties. The study leverages machine learning’s capabilities to quickly detect these trends and link them to highly influential features.
The approach begins with an unsupervised model that clusters wells based on their water cut trends, followed by supervised models that identify the reservoir features most significantly affecting these trends. Wells with similar water cut trends are grouped using time series clustering on oil producers, incorporating dynamic data such as oil, water, and total fluid rates; cumulative oil, water, and fluid production; water-oil ratio; water cut; bottom-hole pressure; and wellhead pressure. Additionally, data and information from water injection wells, including water injection rate and injection pressure, is used to enhance clustering based on areal patterns.
To integrate dynamic and static parameters and establish their connections, a supervised model uses the generated clusters as target and static parameters as inputs. Input data includes matrix and natural fracture properties such as porosity, permeability, rock types, picks and fluid properties, all of which exhibit variations. Data from these static models are prepared using harmonic, geometric, and arithmetic averaging methods to ensure proper input for the supervised model. Classification and Regression Tree (CART) models are employed to determine the relative importance of each variable, identifying those with the highest positive or negative impact on water encroachment. Based on these variables and their associated weights, water influx contour maps are generated for each cluster.
This novel approach not only identifies water encroachment trends in clustered areas but also pinpoints the static properties that most significantly impact these trends using supervised learning models. By utilizing CART, the relative importance of these properties per cluster is identified, allowing for the generation of a water encroachment map that reflect both water cut trend and the influence of key factors.
The generated maps offer deeper insights into water management and surveillance within each cluster area of the reservoir. This methodology provides reservoir engineers with a powerful tool to better predict and manage water encroachment in complex, heterogeneous oil reservoirs.
A Comprehensive Review of Machine Learning Application to Flash Calculations in Compositional Reservoir Simulators
Authors: Farmanov Ravan , Al-Shalabi Emad W. , Elkamel Ali , Markovic Strahinja , AlAmeri Waleed , Venkatraman Ashwin 
Published: November 2024

Abstract
Reservoir engineering often involves dealing with formations that contain several chemical species and show complex phase behaviors. One of the most critical aspects of this field is calculating phase equilibrium, which is usually achieved through numerical simulations of multi-component, multi-phase flow in porous media. These simulations are known as flash calculations and describe the phase behavior of specific fluid mixtures. Flash calculations are typically performed using reservoir simulators that are based on equations of state (EOS), such as the Peng–Robinson (PR) and the Soave–Redlich–Kwong (SRK). While EOS-based flash calculations are known for their accuracy in describing phase behavior within reservoirs, they can be computationally intensive and time-consuming.
Machine learning (ML), a branch of artificial intelligence, offers a promising alternative by predicting desired outputs through learning complex patterns among fluid properties of the reservoir. ML models have the potential to outperform traditional reservoir simulators in predicting phase equilibrium by significantly reducing the computational time required for flash calculations. This paper reviews various machine learning models developed over the years as alternatives to traditional flash calculations. It also explores the application of ML in both stability and phase split tests, discussing their limitations and providing recommendations for further improvements.
Enhancing Pipeline Integrity: Overcoming Challenges of External Corrosion Through Advanced Assessment and Repair Strategies
Authors: Kamonwan R. , Thanapat W. , Piyanut W. , Matina T. , Napatthamon V. , Chatchai L. , Jakraphan J. , Kriangsak P. , Khemmathas S. 
Published: November 2024

Abstract
This study investigates the integrity of a 16″ onshore pipeline in North-East Thailand, which unexpectedly exhibited significant external corrosion despite being equipped with dual protective systems—FBE coating and ICCP. Initial assumptions based on MFL pig inspections of two 10″ pipelines indicated a minimal corrosion threat, aligning with simulated inhibited corrosion rates. However, the 2021 MFL inspection of the 16″ pipeline revealed 2,265 external metal loss features at 1,266 locations, with corrosion depths reaching up to 30% of the nominal thickness, particularly around FJC.
Direct assessments confirmed the accuracy of MFL data, revealing that improper FJC application during installation and CP shield effects were significant factors in the corrosion observed. The investigation also highlighted the limitations of routine CIPS and DCVG surveys in detecting these issues. A new FJC repair system was qualified, and repair methodologies were refined, resulting in over 40% productivity improvement and zero incident cases from 2021 to 2024. This study underscores the importance of regular inspection, maintenance, and the development of robust repair strategies in managing the integrity of aging pipelines. The findings contribute to the field of pipeline corrosion management and offer practical solutions for similar challenges.
Maximizing Value Through Novel Approach to Project Implementation - Alternate Methods to Avoid Shutdown
Authors: Kanna Vinoth , Karam Saif Abdulla , Mukherjee Rahul , Ganesan Suresh , Anwar Syed 
Published: November 2024

Upgradation of the old glycol flash gas flare was a challenging project considering many factors, namely, shutdown requirements, timeline of implementation, HSE concerns in the existing flare system, pre-construction activities, brownfield interconnections, and fit for purpose design. The project was a key HSE improvement project to ensure that the glycol flash gas flare systems operate with all the necessary HSE design elements.
A unique approach was adopted while implementing this project to avoid shutdown and execute the entire project using the mobilization of a mobile flare to keep the plant in operation while the construction activities were in progress. Since this was a new approach extensive meetings and coordination were required to safely execute the project.
This paper defines the overall design basis, the project execution methodology, process description of the natural gas liquids dehydration process, typical list of deliverables generated during the project and value maximization through novel approach for the activities related to Glycol Flare Upgrade. The objective of this project was to upgrade the existing glycol flare and demolish the existing glycol vent stack. The project scope included the installation of new flare components, such as velocity seal, windshield, pilots, thermocouples, HEI ignitors and flame front generators, as well as a new ignition skid with local control panel, conduits and cables. The project also involved the modification of the existing piping, instrumentation and control systems to comply with the latest company standard for flare systems.
The Longest Super Slim 3-7/8in High Angle Slant Profile Drilled Successfully into the Least Stress Direction – Kuwait Case Study
Authors: Al-Ajmi H. , Al-Saadi D. , Shadad N. , Al-Raish A. , Al-Mutairi F. , Nouh W. , Farhi N. 
Published: November 2024

Abstract
The objective is to drill a long super slim 3-7/8in high angle 76º inclination profile to maximize the primary reservoir exposure cutting through a depleted zones in a harsh drilling condition and pressure regimes into the least stress direction.
The actual drilled profile was checked first from surface down to the last 5.0in liner shoe depth at 18,044’ MD to measure the wellbore tortuosity and predict the cased hole friction factor the buckling areas to assure an optimal weight on bit delivery expected while drilling.
A full analysis for the open hole was taken using a nearby wells with similar inclination shorter intervals and were able to derive the expected friction factor to model the right Bottom hole assembly (BHA) with iterations to enhance the capability of drilling a long interval without sacrificing the well stability or directional control to overcome the drilling challenges.
The section length was planned to drill 950ft though the reservoir and so, BHA was designed to have a mud motor to control the directional profile and overcome the high downhole torque expected +/- 6,000 ft-lbs and the sinusoidal buckling was 7k lbs WOB.
The bit selection played an important rule to choose the right PDC bit with the optimum cutter's distribution and rake angle for more aggressive cutting efficiency to drill with the minimum WOB applied.
The reservoir pore pressure and fracture gradient were also derived from the nearby wells to decide keeping optimized mud weight and Rheology for high permeable depleted limestone zones while drilling in azimuth 117º near to the least stress direction in the field.
All the drilling and subsurface challenges were sorted and analyzed with an engineered solutions techniques to overcome and deliver the well to the planned TD at 19,000ft MD with total section length of 956ft meanwhile, keeping an optimized drilling parameters and mud rheology to drill the maximum reservoir exposure in a super slim high angle 76º trajectory.
This paper provides an overview regarding the preparations on how to drill a high angle well profile in a super slim 3-7/8in hole size and how to overcome the challenges in terms of the buckling limit and the narrow channel of a mud weight range using a solution engineering solution technique and how the execution was taken to fulfill the subsurface reservoir requirements.
First time in Kuwait for assigned direction company to drill a long 3-7/8in super slim deep well. Differential sticking hazards were addressed by geomechanical modeling for the depleted reservoir to decide the mud weight window for optimum hole cleaning, Stabilizing the underbalanced formations and controlling the ECD following the best practice for hole cleaning in addition to the usage of the mud motor to overcome the low buckling limit and develop a direction control on the well profile.
Adapting to the Increasingly Stringent Coiled Tubing Inspection Requirements
Authors: Jamoussi Anouar 
Published: November 2024

Abstract
Higher expectations for safety, well integrity and Coiled Tubing (CT) string durability have resulted in increasing demand for periodic inspections and continuous monitoring of CT strings. As such, leading companies in the Middle East are requiring more stringent inspections. In this paper, we outline the challenges presented by stricter inspection requirements amid the recent trend for heavier walled strings. We also discuss the engineering solutions we employ to address these new challenges and share concrete results that proved effective.
The new inspection requirements go beyond the stringent API-5ST-SR37 specification for new CT strings. The trend towards heavier wall thickness in CT strings makes the new specifications even more challenging as this increases the separation between the external inspection sensors and any internal flaws. This separation degrades the sensitivity of the inspection tools to internal flaws. To address these challenges, we revamped our CT inspection and continuous monitoring solutions. Specifically, we have developed the following solutions: A new software version for our ECTAS III inspection system with advanced signal processing algorithms that improve sensitivity to difficult flaws, such as narrow longitudinally oriented mass losses on the outer or inner tubing surface.Significant hardware and software enhancements to our continuous, real-time CT monitoring system.
These enhancements yielded significantly better results as proven by data collected upon scanning third-party certified reference standards. These standards come from CT samples supplied by various mills, are EDM machined with reference flaws and independently certified according to industry specifications that we aim to comply with.
We share the performance results of the scans through plots depicting the Magnetic Flux Leakage (MFL) response of the reference flaws. The results show an increase in tool sensitivity to flaws evidenced by a minimum increase of 63% in the signal to noise ratio (SNR) across key reference flaws. This increase in the SNR depends on the wall thickness of the reference standard as well as the flaw geometry.
Our results show that our probability of detection (POD) has improved dramatically. We are now for the first time able to detect the most difficult reference flaw of the new specifications: the longitudinal inner diameter (LID) flaw. The results highlight the unprecedented achievement of a minimum SNR of 3 to 1 on LID flaws in tubing 2 3/8 inch in diameter and up to 0.276 inch in wall thickness. Our technological improvements have ensured continuation in meeting industry requirements amid challenging changes in CT specifications. We continue to contribute to leading CT operators’ endeavor to ensure higher CT quality, enhance safety and drive operational excellence.
Downtime Avoidance and Inspection Optimization Towards AI Implementation (Online Robotic Inspection)
Authors: Oktawira H. , Alkhoujah K. , Hussain H. R. , Alharbi M. 
Published: November 2024

Abstract
ADNOC Sour Gas is dedicated to driving innovation and growth that will improve our business and operation performance; by leveraging robotic technologies or artificial intelligences, thereby optimizing overall inspection activities by eliminating the needs of conventional inspection technologies which require extensive scaffolding, manpower, and downtime/ plant shutdown. The impact of such applications extends to maximizing the assurance levels on health, safety, and environmental (HSE) measures, by eliminating the requirement for the workforce to enter tanks and accordingly minimizing potential risks. Three (3) storage tanks were chosen for this project. The result was satisfactory, and the technology will revolutionize inspection practices in the near future.
Geological Carbon Storage in Saline Aquifers: Factors Governing Residual & Solubility Trapping
Authors: Kumar V. , Ren G. , Talabi O. , Misra S. , Odi U. , Silver A. , Gupta A. , Temizel C. 
Published: November 2024

Abstract
Carbon dioxide (CO2) storage in saline aquifers holds offers a promising solution for mitigating greenhouse gas emissions. Deep saline formations are considered optimal for CO2 storage given their substantial capacity. This study examines the research on the two primary trapping mechanisms, solubility trapping and residual trapping, which are essential for the secure and stable containment of injected CO2 within saline aquifers. Solubility trapping and residual trapping are rapid and pervasive mechanisms in CO2 storage within saline aquifers. A simulation-assisted sensitivity analysis was performed to investigate how various reservoir characteristics, fluid transport properties, and engineering parameters affect the spatiotemporal evolution and interactions of these two trapping mechanisms under diverse geological, fluid, and engineering conditions. Residual trapping becomes the dominant mechanism for long-term CO2 storage, with volumes generally doubling those of solubility trapping after 50 years of monitoring, even though solubility trapping initially surpasses residual trapping at the end of the injection phase. Water imbibition post-injection notably enhances residual trapping, which more than doubles with an injection rate increase from 1 MMT/y to 10 MMT/y. Initially, higher porosity aquifers have high solubility trapping, but this shifts to significant residual trapping as injection continues and ends. Temperature significantly impacts CO2 residual trapping, with a six-fold increase observed at 40°C after injection. Thicker aquifers exhibit pronounced post-injection residual trapping increases, contrasting with stable solubility trapping levels. High-pressure aquifers maintain higher levels of residual trapping, especially noticeable 15 years post injection. Aquifers with high pressure, high permeability, high anisotropy and low temperature show a drastic increase in residual trapping after injection concludes, highlighting their important role in long-term, stable CO2 storage.