bluuebunny/crossref_metadata_2025 huggingface dataset:
Scrubbing Spacer for Gas Storage Well
Authors: Maarof Mohd Sabri , Siddiqi Faizan Ahmed , Elhancha Anouar , Qasmi Urooj Khalid , Masoud Zuleta Rodny Benjamin
Published: February 2024

In recent years, the utilization of underground gas storage wells in the Kingdom of Saudi Arabia (KSA) has witnessed significant growth. This strategic approach enables the operator to effectively reserve natural gas for the long term and optimize the management of supply-demand dynamics.

In a natural gas storage well, the casing and the cement sheath surrounding it are critical barriers to withstand downhole stress cycles and ensure long term well integrity. Cement evaluation logs are performed, and the cement sheath quality is analyzed to decide whether the well can be completed for gas injection or remedial work is necessary. Hence, a proper cement design plays a vital role in the successful construction of such wells. A major aspect of any cement design is mud removal.

Effective removal of drilling fluids is crucial to a successful cementing operation. However, drilling fluids are becoming more resilient, especially Non-Aqueous Fluids (NAF) with increasingly complex hydrocarbon chains and strong reverse-emulsions. Therefore, it is ever more difficult for conventional spacers to clean NAF-containing wellbores and water-wet casing surfaces, resulting in poor cement bonding and lack of zonal isolation. Conventionally, a surfactant is used to invert the emulsion and water-wet the surfaces, while a mutual solvent is added to disperse and dissolve the oil droplets. Since the formulations of NAF vary greatly, a universal spacer formulation suitable for all applications does not exist.

To solve this problem, the spacer performance is augmented by introducing scrub fibers to promote mud removal. The fibers enhance flow shearing and attract non-aqueous compounds through hydrophobic-hydrophobic interaction. A rotor-cleaning test using a sandblasted rotor facilitates the evaluation the spacer cleaning performance in the laboratory and tune its formulation.

Consequently, the enhanced spacer design has improved cement bonding to casing and wellbore across the critical well interval. The clear improvement seen on the cement evaluation logs has been well recognized. So far, a 100% success rate is maintained.

An engineered scrubbing spacer which includes a fiber in the spacer to substantially improve NAF removal during cementing operations. It has also been shown to improve the removal of water-based drilling fluids that are sticky and difficult to clean.
Visualizing Geoscience Data Interpretation Into Photorealistic Modern Analog Using Generative AI: A Preliminary Result From Carbonate Platform Environment
Authors: Ramdani Ahmad Ihsan , Perbawa Andika , Vahrenkamp Volker
Published: February 2024

Geoscience datasets are fundamental for subsurface investigation. Paradoxically, they are sometimes exclusive and require subject-specific expertise to interpret and visualize. One such example is seismic interpretation. Geophysicists typically reconstruct ancient depositional settings by interpreting a myriad of seismic attributes and drawing analogs to the sedimentary process of the modern depositional environment (Posamentier et al. 2007; Vahrenkamp et al. 2019; Ramdani et al. 2021). Most of these interpretations will likely be reflection amplitude, frequency, impedance, or other geophysical attributes interpreted and "visualized" in the present-day geomorphology context (Posamentier et al. 2007; Warrlich et al. 2019; Ramdani et al. 2022b). The interpreter will then rely on verbal or written descriptions to convey their interpretation. Often, these descriptions are only well understood by fellow interpreters. Attempting to convey the same interpretation to a non-expert requires some degree of visual aid. Thus, a method to picture geophysical signals as a "depositional environment" is needed to bridge this gap. This study aims to leverage the application of generative AI as a tool for seismic interpretation. We propose a Conditional Generative Adversarial Network (CGAN)-based methodology capable of converting seismic attribute maps into photorealistic images of the modern satellite imagery analog as a visual aid for seismic interpretation.
Finding the Next Incident (FtNI): A Tool for Learning from Normal Work
Authors: Yasseen Abdelsalam , Nonno L.
Published: February 2024

The conditions that could lead to the next serious incident in our organizations already exist today; the reason it hasn't yet occurred is simply because the combination of conditions has not materialized thus far.

When contemplating the next incident, important questions arise: What will happen? Where will it take place? Who will be affected? And, most importantly, why can't we anticipate it? If we haven't truly learned enough from previous cases, it raises the question of whether we need more high-potential "near misses" to enhance our understanding and foster better learning.

The diagram, represented as Figure 1, illustrates that incidents constitute only a small portion of the data available for Learning from Normal Work (LFNW).
Increasing Production Through the Application of an Emulsion Viscosity Reducer in an Onshore Gas Lifted Middle East Oil Field
Authors: Caird S. , Sharief R.
Published: February 2024

Viscous emulsions are produced as oil and water are transported from the reservoir through the production infrastructure as a result of the shear forces exerted on the fluids by process restrictions. As fields mature and water cuts in wells increase so does the viscosity of the produced fluids due to the formation of emulsions. This increase in viscosity results in an increase in frictional forces and can lead to slugging and a reduction in production.

Laboratory testing was performed on fresh samples collected in the field to identify suitable Emulsion Viscosity Reducer chemistry by measuring the reduction in emulsion viscosity with the addition of various chemical formulations. Based on the results of this testing a field trial was performed on three wells, applying the selected formulation downhole via injection through the gas lift system. Gas lift compatibility testing was performed on the chemical prior to the trial.

Gas lift injection was demonstrated as a viable means in which to inject Emulsion Viscosity Reducer chemical downhole. A lag of between 12 and 24 hours was observed between chemical injection commencing and changes in the fluids being produced being observed. Emulsion was measured by centrifugal analysis and viscosity using a viscosity cup. Measurements were taken prior to chemical injection commencing and throughout the duration of the trial. The chemical injection was optimised and the effect on emulsion and viscosity observed. Reductions in viscosity of approximately 50% were achieved on each well. Emulsion content was also significantly reduced although some wells displayed more erratic results than others, indicating that slugging was a factor influencing emulsion stability. Oil production was also recorded, and increases observed with up to 10% more oil on average produced during the trial period compared to baseline data.

These reductions in viscosity and emulsion, and subsequent increase in oil production, show that existing artificial lift infrastructure can be utilised to increase production efficiency, opening the possibility for other producers of oil and gas in the region to maximise their existing their assets as water cuts increase.
Downhole Sludge and Deposition - Characterization and Treatment Design
Authors: AlMuhaish S. A. , Al Moajil A. M.
Published: February 2024

Sludge formation in oil wells results in near wellbore flow restriction or causes damage to electric submersiable pumps (ESP), thus, reducing their lifetime and causing operational upsets. Prevention and cleanout solutions of such deposits require understanding of sludge characterization and potential formation mechanism. The objective of this study is to present typical types of oilfield sludge and propose a systematic testing protocol to properly characterize organic/inorganic depositions.

Examples of typical oilfield sludge deposits were obtained for the study that either organic, inorganic, or mixtures of both. Several experimental and analytical techniques were used to develop a testing protocol for sludge characterization and remediation. This include filtration, centrifugation, extraction, solubility,, acid digestion, and geochemical analysis, pyrolysis, thermogravimetric analysis, Saturates Aromaticss Resin Analysis (SARA) fractions, Gas chromatography–mass spectrometry, thermogravimetric analysis, x-ray diffraction/fluorescence. Solvents were obtained characterize and analyze sludge components such as benzene, toluene, diesel, n-pentane, chloroform, dichloromethane, terpene or naphthalene-based solvents, HCl acids.

A sludge characterization workflow and testing protocol was developed that include the following main categories: 1) Sample preparation, 2) phase separation, 3) liquid portion, 4) water portion, 5) organic content, 6) inorganic content, and 7) sludge re-formation. The process provided characterization of the sludge composition and potential forming mechanisms such as inorganic, organic, water content and compositions. Most oilfield sludge deposits are mixtures of organic and inorganic components. Implementing systematic analysis procedures helped to better characterize the sludge samples. For instant SARA analysis showed presence of asphaltene in a sludge sample while Gas Chromotography (GC) and Gas Chromotography Mass Spectrometer (GC-MS) analysis suggested presence of synthetic polymer used in drilling operations of characteristics similar to ashapltene. In another example, treating a sludge sample composed of organic and inorganic components with only diesel, mutual solvent, or naphtha feeds was sufficient to partially dissolve and liquefy the sludge, thus, allowing it to be flowed back during production, and eliminating the risk of using HCl acid in acid-sensitive formations.
A Data-Driven Hydrate Plug Detection in Offshore Gas & Condensate Flowlines
Authors: Della Pietà A. , Galimberti C. , Corneo A. , Scaringi S. , Calzavara G.
Published: February 2024

In multiphase transport pipelines, gas hydrate blockages are a major flow assurance challenge, due to the difficulties in remedial actions and the potential massive production losses. Scope of this study is to define an innovative data-driven methodology to early detect hydrates formation, provide an alarm, and permit early intervention before the complete blockage of the flowline.

The proposed approach is applied on a Gas & Condensate pipeline during cold restart, which is the most critical scenario for hydrates formation. In the absence of reliable field data, the methodology was validated on synthetic data. Through a Design of Experiment (DoE) strategy, a wide range of operating conditions (with and without hydrate plug) has been simulated using a multiphase flow model, by varying some key parameters. Only field-measurable variables have been considered for the machine learning model training. In addition, a custom "Friction Factor" indicator and its derivative over time have been calculated, as they emerged to be crucial for model’s performance enhancing.

A classification model (XGBoost), called "Alarm Model", was defined to detect the formation of a hydrate plug and raise an alarm, based on a "RiskProbability" estimate. As a result, the model consistently managed to detect in advance the formation of hydrate plugs, particularly in cases of long-time formation.

A second model (XGBoost), called "Failure Temporal Distance Model", was developed to classify the system status after an alarm is raised, giving information about the residual time to reach the failure event. This model showed good performances, with 85% of recall for the most critical class (i.e., imminent events), and a global accuracy of 80%.

As a conclusion, this research highlights the successful application of machine learning and the relevance of the "Friction Factor" derivative in dynamically detecting plug formation in pipeline systems, without relying only on static thresholds. The use of DoE methodology has proven to be useful in obtaining sufficiently diverse simulations to achieve an algorithm that provides accurate and timely predictions. These findings contribute to the advancement of plug formation detection techniques, with potential applications in enhancing the operational efficiency and maintenance strategies of pipeline networks.
Characterizing Thermal Runaway of Reservoir Rocks Under Electromagnetic Irradiation Towards Hydrogen Generation from Petroleum Reservoirs
Authors: An Baizheng , Yan Keju , Yuan Qingwang
Published: February 2024

In-situ hydrogen generation and extraction directly from petroleum reservoirs provides a new solution to meet the increasing need for clean energy and to mitigate greenhouse gas emissions. This method leverages the abundant petroleum resources while simultaneously sequestrating carbon by-products underground. To enable in-situ clean hydrogen production from petroleum reservoirs, we proposed electromagnetic (EM)-assisted catalytic heating technology. Although significant potential has been recently assessed through lab-scale experiments and preliminary techno-economic analysis, the heating behaviors and interactions between EM waves and reservoir rocks are poorly understood, especially at a high enough temperature when hydrogen is generated. This study aims to elucidate the underlying mechanisms regarding the heating performance of sandstone and shale rocks under microwave irradiation. Advanced characterization techniques are employed to analyze the changes of rocks before and after microwave heating. The thermal runaway (TR) phenomenon is identified for the first time for San Saba sandstone rocks at 568°C and Mancos shale rocks at 253°C when they are exposed to microwave radiation. We further investigated the interactions of microwave with each pure mineral (e.g., albite, chlorite, illite, microcline, dolomite, kaolinite, calcite, and quartz) using a microwave reactor system. We identified that chlorite, albite, and illite are the main minerals that lead to the TR phenomenon. We also found that a high temperature can be easily achieved for both rocks at a much lower input power after TR, resulting in approximately 50% energy saved when the reservoir rocks are re-heated to 500°C. The occurrence of TR can therefore enhance the heating efficiency of reservoir rocks, reduce energy input, and significantly decrease the cost of in-situ hydrogen production from petroleum reservoirs using our proposed EM-assisted catalytic heating technology.
Paradigm Shift of the Deep Marine Fan Deposit (Ultra-Tight) Gumai Sand Reservoir’s Development Upon Recently Known Subsurface Heterogeneity and Uncertainty in Tempino Field, Jambi, Indonesia
Authors: Rahadian R. , Sipahutar H. , Hendrian D. , Priatama I. N. , Hakimi D. A. , Pratama H. , Alfajri R. , Subhan M.
Published: February 2024

This study focuses on gaining a deeper understanding of the Gumai sand reservoir in the Tempino Field, which is known for its complexity due to its heterogeneity and low quality properties. The research draws from insights gleaned from the drilling of six recent wells, shedding light on key factors that have a substantial impact on reservoir behavior and development strategies. One of the central findings of this study is the importance of rock typing. Historically, a simplistic single-rock-type model was used in reservoir characterization. However, the research demonstrates that this approach was inadequate in capturing the reservoir's complexity. By utilizing electrical open-hole logs and taking into account limited core analysis data, the study identifies three distinct rock types within the Gumai sand reservoir. This discovery is a significant departure from conventional practices and underscores the necessity of considering heterogeneity when dealing with sand reservoirs. Furthermore, the study delves into fracture pressure measurements and their implications. The discrepancy in fracture pressure data, observed at an equivalent depth of 1100 meters among the latest five wells, is discussed. This discrepancy is closely linked to the behavior of hydrocarbon production. It's a reminder that understanding fracture pressure is critical when evaluating a reservoir's behavior and productivity.

One of the noteworthy findings pertains to well T-220, which was positioned at the peak of the Gumai sand reservoir's closure. While based on a study conducted in 2022, it was expected to have the highest oil production rate, the actual results were disappointing. Even after a workover job with remedial cementing, the production behavior remained subpar. This observation highlights the importance of rock typing and how it directly correlates with the reservoir's production behavior.

In essence, this research offers a novel and different approach to managing a mature field with diverse, low-quality sand reservoir properties. It challenges the conventional paradigm by advocating for a more nuanced understanding of the heterogeneous sand reservoir. By acknowledging the presence of distinct rock types within the reservoir and accounting for their impact on production behavior, the study paves the way for more effective field development strategies.
Successful Field Trials for Tracking Casing Exit: Innovative Sensing Technology Application
Authors: Gamal Hany , Gomez Hector , Alshalan Meshari , Alshaarawi Amjad
Published: February 2024

Casing exit operations are a common technique in the well intervention domain due to many technical purposes as accessing more reservoirs (re-entry drilling) or bypassing downhole obstructions (remedial operations). The casing exit is a complex operation that involves milling through the well casing to create an exit path and then drilling a rathole for accommodating any subsequent drilling bottom hole assembly (BHA) such as mud motor or rotary steerable system. Accurate tracking of the casing exit process is essential to ensure the operation's success and save extra runs to check passing through the window. This abstract presents two successful field trials for a novel approach using advanced sensing technology to track the casing exit window.

Many factors impact successful window milling in terms of operation parameters, penetrated formation, casing and cement integrity, wellbore inclination and dogleg severity, subsequent BHA, etc. To date, there is no standardized application for tracking window milling, leaving a critical gap in the industry's ability to track this crucial process. Through this paper, innovative sensing technology was tested through two field trials to track the casing exit window through providing the angle change above the window and at the window end along the window profile. The innovative approach showcases the developed technology's remarkable capability to accurately assess the window quality, thereby effectively reducing additional run costs and time.

The field trials conducted on two wells have demonstrated the system's robustness and effectiveness in accurately tracking the milling window during casing exit. The two trials were conducted through casing exit jobs while the sensor was assembled with the milling string. The results obtained from the two jobs through angle differences measurements showed the good window quality to pass the BHA from the technical engineering and operation aspects. The subsequent BHA runs confirmed passing the window milling successfully and the next section was drilling within the same run. The successful implementation of this novel application represents a significant advancement in the field of casing exit operations. It offers tangible benefits to the oil and gas industry by tracking the window profile quality. The remarkable outcomes obtained from the successful field trials unlock opportunities for further advancements in downhole monitoring tools and subsequent real-time log data, leading to a path of increased automation and optimization in casing exit operations.
An Integrated Interdisciplinary Approach to Delivering Successful Infill Wells in a Mature Field
Authors: Yeoh X. , Iliani N. , Fadjarijanto A. , Gallagher B. , Bakar A. , Sikandar A. , Hoeyland H.
Published: February 2024

Jadestone Energy's main approach in maximizing recovery and extending mature field life is the integration of the various subsurface disciplines to improve understanding of the field development and production history. It is critical to capture the reservoir complexity and dynamic behavior to identify the remaining oil potential for future infill target opportunities.

An integrated interdisciplinary study was conducted to capture the reservoir complexity and understand the dynamics of the rock-fluid interaction leading to identification of bypassed oil volumes. Challenges addressed are structural and well placement uncertainties, non-reservoir glauconite distribution, water sweep at local and reservoir scale. Further re-evaluation of 3D seismic and understanding of historical and current production performance supports the infill targets. Fast cycle times between geoscience and well designs criteria resulted in optimum well trajectories for drilling.

Jadestone Energy acquired Stag field in 2017, located offshore Western Australia in the WA-15-L production license. Jadestone Energy has successfully drilled the first well, 49H in 2019 and replicated the success in 2022 by drilling two additional infill wells, 50H and 51H. 50H was strategically drilled perpendicular to existing wells to maximize reserves, resulting in severe anticollision provisions to drill across 10 wellbores within a narrow vertical window. This required careful planning and precise execution of the plan to stay in the sweet spot. 51H was considered as extreme- reach-drilling with the longest production section at shallow vertical depth. Drilling through low reservoir pressure, thin oil column was also impacted by risk of hydraulic fracture which required detailed geomechanical study.

Both wells were drilled safely and achieved the desired outcomes. Reservoir mapping tools were utilized to assist in accurately placing and steering the wells within the reservoir's sweet spot. As a result, 50H inversion results affirmed water cone of 75m when crossing in the vicinity of active producer, matched model prediction for initial rates and water cut, while confirming thicker oil column at toe of the well. It proved the well placement uncertainties of the existing well as per prediction. 51H was drilled safely to target length despite suffering total losses before entering the reservoir. This too confirmed pre-drilled oil column thickness and unique structural prognosis.

The success of an infill well drilling campaign was achieved through applying multi-disciplinary integrated solutions in a complex geological and production environment, coupled with overcoming challenging drilling operations through aggressive well planning and optimization. Successful application of deep resistivity reservoir mapping tools with geosteering was achieved for both wells.
An Experimental Study for the Impact of Heating and Cooling on Strength of Unconventional Reservoirs
Authors: Khan F. , Mahmoud M. , Patil S. , Raza A. , AlTammar M. J. , Murtaza M. , Al Marri M. , Al Shafloot T.
Published: February 2024

Unconventional reservoirs (shale and tight sandstone) have gained significant attention in meeting increasing energy demand despite unconventional ways to exploit these resources. Numerous studies have been carried out to evaluate different aspects of unconventional reservoirs including geomechanical characteristics at high pressure and temperature. However, less attention has been given to understand the impact of temperature change on the strength of the rock. This study focuses on two unconventional rocks namely Kentucky Sandstone (0.98mD) and Eagle Ford Shale (21.39μD), aiming to investigate the effect of heating and cooling on strength. XRD analysis showed that the main mineral in the tight sandstone is quartz (61%), whereas in the shale, it is calcite (88.6%). A central hole was drilled in a 2.5-inch core to accommodate a temperature logger and heated to 250 °C. The rate of reduction in rock temperature was then recorded using a temperature probe till 30 °C. Micro CT-Scan imaging was performed before and after heating the samples to 250 °C to observe any microstructural changes. Finally, the strength of the rocks was determined through a scratch test. The obtained results demonstrated that the sandstone core exhibited a heating rate of 2.3 °C/min initially to reach up to 68 °C, then reduced to 0.8 °C/min to reach the final temperature of 85°C. In contrast, the Eagle Ford Shale showed a consistent single heating rate of approximately 1.7 °C/min to reach 85 °C. The final temperature was set to maximum value of 85 °C during heating due to limitation of temperature logger inside the oven, however for rate of cooling the samples were heated in the oven at 250 °C and cooled at room temperature, measuring the rate of cooling. Comparatively, sandstone was more conducive to heat than shale. During the cooling phase, the temperature of sandstone initially reduced at a rate of 6.5 °C/min to reach 110 °C, 2.7 °C/min till 60 °C, and eventually reached 30 °C at a rate of 0.7 °C/min. The Eagle Ford Shale exhibited a temperature reduction trend, ranging from 5.7 °C/min up to 110 °C, 2.0 °C/min till 60 °C, and reached 30 °C at a rate of 0.6 °C/min. The heating and cooling cycles resulted in reduced strength for both samples. The average strength of Kentucky Sandstone decreased from 5896 to 5415 psi (8.1% ↓), while the strength of Eagle Ford Shale decreased from 18240 to 16840 psi (7.7% ↓). These findings contribute to a better understanding of induced thermal impact on mechanical properties of unconventional rocks.
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

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

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

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

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

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

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

The Haynesville Shale formation in the United States presents unique challenges during completions, the two primary examples of this being high formation temperature of >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

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

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

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.