bluuebunny/crossref_metadata_2025_split huggingface dataset:
Advanced Corrections of LWD High-Resolution Resistivity Images in Hostile Downhole Environments
Authors: Manuaba Ida Bagus Gede Hermawan , Aljishi Mohammad Kadhem , Ritzmann Nicklas , Gouda Mohamed 
Published: February 2024

Abstract
High-resolution resistivity images can be impacted negatively by several factors, especially in high-conductive water-based mud (WBM) systems. Additionally, borehole and drilling environmental parameters like elevated levels of drilling dynamics, multiple rotation regimes of the drill string, the logging while drilling (LWD) imager's eccentricity and excessive salinity are major factors, if uncontrolled while drilling, can result in severe image quality degradation. Even though such control is desired, it is not always possible. To overcome these conditions and their related artefacts, this article demonstrates a recently developed correction method to restore an acceptable definition of fine features from such heavily affected images.
The implemented, multi-stage, correction methodology is based on the LWD raw image dataset and comprises a series of sequential corrections based on the examined environmental artefacts and their severities. The image correction is conducted in a strict order to take most effect. For full transparency of the correction, all intermediate steps are available for quality control purposes and enables the user to potentially improve certain correction steps if required. The final correction level is one image with the most detail restored possible, honoring the data contained in the raw dataset.
This study focuses on the specific objective of identifying fine structural features in slim lateral boreholes, drilled through carbonate reservoirs under uncontrolled and unfavorable environmental challenges. These conditions are a challenge for high-resolution LWD resistivity imager data. The outcome of the correction methodology results in a remarkably sharper definition of fractures and faults (high-dip and high-contrast features) and reduction of the artefacts of LWD imager/string eccentricity effects. In addition, recovery of most of the lost information caused by severe drilling dynamics and associated image dataset spike artefacts. A comparison of structural features from the final corrected LWD image dataset to wireline (WL) imager was conducted and good repeatability was concluded between both different technologies with similar WBM salinity properties.
The methodology enables the LWD WBM high-resolution resistivity imagers to deliver up to standard image quality in extreme downhole drilling and environmental conditions.
Notional Development Concept Screening using Probabilistic Workflow for Prospects & Greenfield Projects
Authors: Sinha A. K. , Jalan S. , Garimella V. S. S. 
Published: February 2024

Abstract
Identification and screening of development concept options is one of the critical steps during early stage of field life cycle (exploration and appraisal phase). This requires comprehensive understanding of uncertainty range in field parameters that may impact the recovery factor and conventional workflows may take many weeks to quantify the impact of each parameter on ultimate recovery. The proposed probabilistic workflow has been automated through efficient use of open-server application to accelerate the screening for faster decision making.
This methodology consists of nine (9) steps for identifying list of low/base/high development concepts for any prospect or greenfield. The detailed description of the 9 steps will be provided in the paper. In summary, Steps-1 & 2 focus on identifying the relevant subsurface data and defining the best technical estimate model. Step-3 outlines the selection of key parameters that have a significant impact on the recovery factor. Probabilistic distribution function is used in Step-4 to define range of recovery factor for the prospect. Three representative models are selected in Step-5 and estimated recovery factors are also compared against benchmark values in Step-6 to ensure forecasts and recovery factors are reasonable. From a range of development options identified in Step-7, notional economic model is set up for techno-economic evaluation of the likely development scenarios in Step-8. Previous 8 steps enable selection of optimal development cases in Step-9.
Six (6) steps of this workflow have been automated through integration of OpenServer application with pre-defined well, tank and network models. An integrated user-friendly spreadsheet is used to list all the parameters, generate the scenarios, and present results. This has facilitated to evaluate all development concept options within a week of model setup.
Application of probabilistic workflow in exploration prospects has facilitated an end-to-end uncertainty management from in-place volumes range to recovery factor estimation and finally leading to development concept screening. This workflow can be applied to sizeable and/or strategic prospects to provide a comprehensive informed decision making on the way forward for resource maturation. The proposed probabilistic methodology for prospect evaluation followed by an appropriate development concept is quite generic and can be easily applied across the exploration to development stages of oil and gas industry.
Fit-for-Purpose Sweep Pumping Schedule to Improve Operation Efficiency While Achieving Effective Hole Cleaning During Clean Up Cycle at Well Total Depth in an Unconventional Drilling Operation
Authors: Yin G. , Rogers S. W. , Le V. Q. 
Published: February 2024

Abstract
Many horizontal wells have been delivered in the unconventional drilling operation. After the well is drilled to total depth (TD) of the well, it is not uncommon to have an excessively long clean-up circulation time prior to tripping out of hole to prepare for completion run. To improve the operation efficiency, an innovative strategy was proposed and applied by optimizing the sweep pumping schedule to reduce the circulation time while achieving the equivalent effective hole cleaning.
In this paper, an innovative application tool developed to accomplish this new pumping schedule will be addressed. When the rig follows the new pumping sequence established by this tool, multiple tandem sweeps can be pumped in the hole while keeping only one tandem sweep in the annulus between the end of curve (EOC) and surface, and one tandem sweep between surface and EOC inside the drill string. As such, the well control condition will not be compromised, the equivalent hole cleaning effect can be achieved while reducing the circulation time by around 50%. In addition to the benefit as described above, this tool also brings the great convenience to eliminate the time-consuming calculation for driller, foreman and mud engineer, which allows the team to increase focus on operations.
Real-Time Detection of Stuck Pipe Utilizing Hybrid AI-Physical Prediction Models
Authors: Malki Mohammed A. , Abughaban Mahmoud F. , Alshawabkeh Albara' A. , Teixeira Guimaraes Thiago 
Published: February 2024

Abstract
Effective stuck pipe prediction becomes more challenging and requires real-time advanced analysis of all available drilling data. This paper presents an innovative model to predict stuck pipe incidents. A machine-learning model based on intensive feature-engineering integrated with physical models has been developed. It automates real-time drilling data collection, analysis, and detects the patterns for the most dominating drilling parameters values to achieve the success criteria of early warning signs of stuck pipe incidents. It has been applied on two equal sets of wells either stuck or non-stuck incidents. The model triggers alarms reliably and early before the stuck pipe incidents happen and therefore corrective actions could be taken properly in advance.
Evaluation of Machine Learning Techniques for ESP Diagnosis Using a Synthetic Time Series Dataset
Authors: Alhashem M. , Lastra R. , Ahmed M. , Ghouti L. 
Published: February 2024

Abstract
Electrical submersible pumps (ESPs) play a pivotal role in oil and gas extraction, elevating large volumes of fluids from subsurface reservoirs to the surface. The ESP is the primary method for artificially lifting oil in the Middle East to meet production demands. The reliable operation of these pumps is paramount for optimizing production and minimizing downtime.
Despite the critical importance of ESPs, there has been a gap in the literature concerning the application of machine learning techniques exclusively focused on synthetic digital twin data for condition monitoring and fault diagnosis in the oil and gas sector. This study fills this gap by conducting a comprehensive evaluation of machine learning algorithms, specifically Gaussian Mixture Models (GMM) and Support Vector Machines (SVM), for ESP diagnostics. Results indicate that GMM outperforms K-means in anomaly detection, achieving an accuracy rate of 91%, while K-means reached a maximum of 49%. In multi-class event classification, SVM classifiers demonstrated high efficacy, with an F1-score reaching up to 94.35%. These methods were rigorously assessed through cross-validation, utilizing evaluation metrics such as accuracy, precision, recall, and F1-score. The study not only underscores the efficacy of GMM and SVM in ESP diagnostics but also introduces a novel approach to leveraging synthetic digital twin data for condition monitoring in the oil and gas industry.
Ultra Deep Reservoir Mapping Allowed for Successful Real-Time Azimuthal Geo-Steering Decisions in Channelized Reservoir Structure
Authors: Mohamed Ramy Abdelkader , Khamry Ayman , Dolan James , Santoso Gagok 
Published: February 2024

Abstract
This paper presents a proactive geosteering approach in stacked channels using Ultra-Deep Azimuthal Resistivity (UDAR) 360 technology. This technology provided the ability to map reservoir quality in 360 and allowed optimum azimuthal steering decisions to maximize exposure.
Three novel approaches are introduced in this paper; the first was the use of high-resolution 1D reservoir mapping with Deterministic Parametric inversion (DPI), enabling comprehensive structural delineation without compromising drilling speed. The second was the 2D Transverse inversions in real-time, allowing for interpretation of the properties left/ right around the wellbore. The last was the real-time (mapped/inverted) resistivity volumes delineating the channel sand structure. Combining these novel approaches allowed for a full suite of UDAR inversions that was used to make the real-time azimuthal geosteering decision in this field. The UDAR inversions enabled the geosteering team to map boundaries, optimize the well placement in each target channel, and map the presence of fluid contacts.
The subject well is located on the flank of the field and the structure. This presented challenges in characterizing the target properties, such as continuity, fluid movement (lateral water encroachment and water fingering), and target channel recognition. The real-time UDAR 2D Transverse inversions and resistivity volumes were used to make azimuthal geosteering decisions to place the well in the best target zone. These real-time approaches enabled a complete azimuthal geosteering service 360 degrees around the wellbore. This allows for optimization of the well position in the best quality zone in any subsurface direction around the wellbore. This deployment represents a landmark in interactive azimuthal geosteering decision-making using UDAR technology in real-time and demonstrates the close collaboration, teamwork, and integration necessary to drive innovation.
The success also enabled the project team to quantify the savings of carbon footprint from both operational efficiencies, digital transformation, and productivity. Integrating the 1D / 2D inversions and the resistivity volumes allowed for a full interpretation of the target structure, exceeding the development goals.
Optimizing Frac Plug Drill-Out and Clean-Out Performance in High Pressure/Temperature Extended Reach Laterals Using Hydraulic Completion Rigs
Authors: Vinasco S. , Tourigny M. , O’Brien M. , Frantz J. , Hollerich K. , Griffith J. 
Published: February 2024

Abstract
The Haynesville Shale formation in the United States presents unique challenges during completions, the two primary examples of this being high formation temperature of &gt;300 F and high formation pressure of 7,000 – 12,000 psi at depths of 10,000 – 13,000 ft. Oil and gas operators are drilling lateral lengths between 9,000 and 15,000 ft. This analysis will focus on optimizing frac plug drillouts (also known as mill out) and cleanouts of frac plugs in extended reach laterals under high pressure and high-temperature conditions using Hydraulic Completion Units (HCU). In the preplanning phase of the operations, torque and drag models were created for these wells to obtain projected data to compare with field data. Field data was collected by pressure transducers installed on the Hydraulic Completion Units providing rotary torque, casing pressure, pump pressure, and hookload values on a per-second basis. In addition to analyzing this field data, a selection of wells from other basins with less extreme formation temperature and pressure parameters are utilized to compare against. During the post-job phase, operational efficiencies are compared amongst these mill outs to hypothesize about any unknown factors outside of the collected data that may have affected performance.
Maximizing Well Delivery Process Efficiency and Driving Cost Optimization Using Well Functional Requirements Tool (WFR Tool)
Authors: Lawati Yasin Al , Rubaiey Hilal Al , Braiki Abdullah Al , Naabi Hashil Al , Abri Humaid Al 
Published: February 2024

Abstract
Petroleum Development Oman (PDO) is the leading oil and gas production company in the Sultanate of Oman. The company has a great technical focus enabler for delivering hydrocarbons efficiently and safely in the Sultanate through different tools and technologies. Well Functional Requirements (WFR) project is one of these key tools that has been implemented recently in PDO. The project aims to capture the basis of well designs, maintain high data quality, apply adequate level of assurance, and utilize the assured data envelope to control well risks, optimize well design and operations, and achieve process efficiency and commercial targets.
To achieve the highlighted targets, an in-house tool was developed, the WFR tool. The WFR tool acts as a platform for collecting, maintaining, and assuring the well functional requirements. This includes well targets, geological prognosis, pressure and temperature regime, production data, risk register, and other critical inputs required for developing a well design. The tool enables an easy and accurate data front loading through data drop drown lists, data import functions, system alerts, automated tabs and a detailed standard operating procedure. In addition, the tool allows to update and track functional requirements indicating the actual changes over a defined period of time.
The tool has been applied in multiple fields since 2022 across the company including exploration and development fields and showed very good results. In terms of well delivery process efficiency, the WFR tool has provided the ability for the Planning and Design Engineers to collect the functional requirements with less effort, maintain them in a high-quality manner, get them assured and successfully define their wells design envelopes for the upcoming scopes. In terms of cost optimization, knowing the design boundaries and being able to track them has helped the Planning and Design team to properly select the wells materials and services avoiding over or under designing approaches. Furthermore, the tool has provided additional features such as "Compare Data" feature which enabled monitoring data ranges, flagging thresholds, and testing design robustness. The next phase of the project is targeting full integration with the other well delivery process tools and optimization of the existing materials and services contracts to make them fit for purpose.
The tool is a new way of managing well designs boundaries and evaluating their impacts on processes, operations, resources and contracts. The WFR tool captures the assured data (formation tops, pressure prediction, subsurface risks and other inputs) and provides them as an operating window for planning and execution phases and provides means of monitoring the window limits and identifying impacts on well design and cost.
While Fracturing Stage Evaluation Workflow
Authors: Ghuwainim Y. M. , Ibrahim A. F. , Ali M. M. 
Published: February 2024

Abstract
The goal of hydraulic fracturing in nano-Darcy rock is to create an artificial reservoir, known as a stimulated reservoir volume (SRV), in the source rock. The SRV created in each stage varies in terms of pore-throat size, fracture dimension, and production contribution. Existing evaluation techniques for unconventional hydrocarbon reservoirs, such as rate transient analysis (RTA), chemical tracers, micro-seismic, and fiber optic cables, require long production data, are performed after the hydraulic fracturing job, require extended analysis time, or are expensive to implement (Thompson et al., 2017; Kumar & Sharma, 2018; Ashry et al., 2021; Barree et al., 2002).
Ibrahim et al. (2020,2021) proposed a technique that provides characterization of hydraulic fracture stages during the plug and perf treatment. This technique, coined here ‘While Fracturing Stage Evaluation’ (WFSE), can estimate several parameters, including permeability, fracture geometry, stimulated surface area, and cluster efficiency for each evaluated stage. The required data for WFSE are treatment time, bottomhole pressure, and hydraulic fluid injection rate. The proposed technique is based on falloff analysis, a well-known practice in well testing. However, in WFSE, the fall-off methodology is applied to characterize fracture attributes for individual stages rather than for the entire well (as in well testing). Additionally, this paper provides a new interpretation of instantaneous shut-in pressure (ISIP), in close agreement with WFSE results and provides valuable insight on the growth of the hydraulic fractures.
The new workflow proposed in our study was applied to a hydraulicly fractured well with 41 stages. The results matched the fracture half-length and Ac√k obtained from conventional RTA in 90% of the cases. The results from both techniques led to the identification of fracture geometry (complex or planar fracture), flow regime (spherical, radial, and linear), and fracture parameters (SRV, Ac√k, Xf), which provided an improved understanding of the fracture surface area. The prposd workflow can be used to optimize the design of stimulation, completion, and simulation modeling.
Nonintrusive Rapid Evaluation of Fracture Isolation in Plug-And-Perf Completions Based on Data Science Methodology
Authors: Oparin Maksim , Crawford Elspeth , Alzahrani Bader , Tineo Roberto , Najmi Asim , Enkababian Philippe , Borisenko Alexey , Fedorov Andrey 
Published: February 2024

Abstract
Plug-and-perf is a commonly used technique to complete wells in tight reservoirs. Plugs are used to ensure zonal isolation along the lateral for proper stimulation. Plug failure can lead to understimulation of a fracture stage and overstimulation of a previously stimulated zone. Identifying plug failures using only pressure behavior can be time consuming and, in many instances, misleading as pressure drop can be attributed to other aspects of the fracturing process. The objective of this work is to implement a nonintrusive technique to effectively identify plug failure in near real time to support the decision making during fracturing operations.
An innovative data science–based methodology is used for high-frequency surface pressure oscillation decomposition and interpretation during the hammer immediately after shut-in. An advanced algorithm identifies the fluid entry depth and estimates the probability of the treatment going to the intended stage or a deeper zone. Before the main fracturing treatment, the hammer of a prior short injection test is analyzed, and fluid entry depth is estimated. Depending on the probabilities of the fluid being injected into the target zone, the decision is made whether to run another plug or continue with the main fracturing.
Several hydraulic fracture stages were monitored following this methodology and results were compared to multiple conventional criteria such as ball landing differential pressure, pressure drop magnitude during injection, pressure drop rate, and treating pressure level at the target rate. The ability to deploy a plug or not below the deepest cluster was also used as an indication whether there was a fluid flow down to the previous zone.
Twenty-four stages showed proper isolation, with 83% successfully identified using the data science approach. Similarly, six stages showed indication of communication to a previous zone with 67% of these events detected. The analysis proved effective, and interpretations were completed within 15 to 30 minutes, preventing operations delays. The quality of the hammer effect could be detrimental to the interpretation; therefore, a second short injection test may be necessary in some cases.
This innovative methodology can benefit completion engineers in evaluating the integrity of the frac plug isolation in a timely and effective manner enabling operation optimization.
The paper utilized two separate wells to evaluate the efficacy and usefulness of this methodology to see field operational capability and real world application.
Defying the Creaming Curves: Shuaiba Stratigraphic Play in PDO, Sultanate of Oman
Authors: Shen C. , Hasler C. A. , Al-Ghaithi Y. , Harthi A. R. , Loubens E. , Roberts J. , Shaibani M. , Rawahi Z. , Mazrui M. 
Published: February 2024

Abstract
Shuaiba formation is one of major oil plays in North Oman and across the region. More than 4 billion barrels in-place oil have been discovered over the past 60 years within Block-6. In the late 90's and early 2000's, the play was thought to be largely creamed and the remaining potential was thought to be trivial and mainly located in areas of low relief which would result in economically challenging discoveries with small volumes.
This pre-conceived view on the Shuaiba play changed with the advent of large 3D wide azimuth (WAZ) seismic data in the last decade linked with large WAZ acquisition campaign in North Oman. In addition, many wells targeting deeper formations penetrated low relief (&lt;15m) Shuaiba structures with encouraging mud gas readings, which when logged found unexpected pay. This together with the advances in seismic processing/imaging and the building of a comprehensive depositional stratigraphic framework led to the proposal of a new stratigraphic play concept in 2020 and was tested with an aggressive exploration and appraisal campaign. The new concept was proven through two discoveries in 2021 delivering attractive recoverable oil volumes which yield high development net present values (NPV) at competitive unit technical costs (UTC). A phased development approach with early monetization of the exploration/appraisal wells is in place for both opportunities supporting PDO's production growth strategy.
With the continued focus in PDO on resilient and attractive projects, Shuaiba as shallow carbonate play will continue to play an important role in sustaining exploration and development activities. To support that and building on the success of this stratigraphic play concept, a Shuaiba play portfolio rejuvenation work was carried out in selected areas resulting in significant recoverable volume addition to the portfolio. These volumes will support the exploration drilling plans in the upcoming 2-3 years. The concept is also being investigated over other white space area's where seismic acquisition or processing is underway. These will further contribute to the future business plan for PDO and Sultanate of Oman.