bluuebunny/crossref_metadata_2025 huggingface dataset:
Implementation of Petroleum Industry Act 2021 - A Pathway for Economic Prosperity and Sustainable Development in Nigeria
Authors: Nwankwo O. K. , Ringim A. U.
Published: September 2024

The enactment of the Petroleum Industry Act 2021 (PIA) reformed the Nigerian petroleum industry from a single regulatory framework that was in effect for over 50 years to a dual regime, by introducing clear distinctions between the streams of petroleum operations that was previously not clear-cut and the creation of two distinct and focused regulators for the upstream value chain and for the midstream and downstream. The objective of this paper is to review the PIA Best-In-Class Regulatory Framework for achieving a path to economic prosperity and sustainable development in Nigeria. Furthermore, the lesson learnt and experiences from introducing a new approach and transitioning from a single to a dual regulatory regime was also reviewed. The objectives of this paper were achieved through the comprehensive review of the updated regulatory structures, general administration and functions of the regulators that provides clear focus for industry stakeholders for investments opportunities and benefitting from the reforms of the PIA. Additionally, comparisons of the provisions of the Petroleum Act of 1969 to the PIA, the change programs of the PIA Transition committee, the scope of work and terms of reference of consultants saddled with handling some change management programs and the industry and regulators responses to the introduction of a new approach was also studied. The paper found that the PIA addressed the operational deficiencies of the previous regulatory regime by the recognition of distinct streams on the petroleum value chain established through custody transfer, provided clear focus for the regulators and industry stakeholders in the light of energy transition, unbundled the licensing system that hitherto exist, and entrenched transparency in hydrocarbon accounting. Therefore, the implementation guarantees Nigeria optimized value chain, energy security, improved government revenue, expansion of industry for employment. However, despite the clear benefits, change management related to strategic communications, trainings and transition of industry experience from previous regulatory framework is a big factor to prevent regulatory uncertainty. Recognition of distinct streams for value chain optimization offers advantages and provides pathway for economic benefit and sustainable development. Lastly, the paper finds and recommends that any regulatory transitioning, effective implementation of new approaches and attainment of objectives and benefits of such reforms must be accompanied by detailed change management program.
Success Cases and Lessons Learned After 20 Years of Oilfield Digitalization Efforts
Authors: Saputelli L. , Duran J. , Rivas F. , Casas E. , Ramos L. , Bravo C. , Chacon A. , Temizel C. , Mubarak S. , Chacon J. , Escorcia A. , Elred M. , Diaz S. , Yrigoyen A. , Moricca G. , Querales M. , Lopez C.
Published: September 2024

The energy transition presents a critical juncture for the oil and gas industry. As we navigate toward a more sustainable future, digital oilfield (DOF) technologies play a pivotal role in optimizing operations, reducing environmental impact, and ensuring energy security. This paper aims to explore best practices and lessons learned from two decades of digital oilfield (DOF) implementations. The scope includes analyzing successful case studies, challenges faced, and practical guidelines for short-term DOF adoption in the context of the energy transition.

Our approach involves a comprehensive literature review, examining industry reports, academic studies, and real-world experiences. We will synthesize insights from DOF projects, emphasizing data integration, change management, and interdisciplinary collaboration. The process includes distilling actionable recommendations for future implementation. Through our review, we anticipate uncovering key success factors and pitfalls in DOF initiatives. We will present practical observations on data governance, cybersecurity, and strategic alignment. Additionally, we aim to draw conclusions regarding the adaptability of existing DOF practices to the evolving energy landscape.

Value creation from digital oilfield applications exceeded all our previous expectations. Over the past 20 years, DOF initiatives have evolved significantly. Notable achievements include the improved decision-making through streamlined processes and automated workflows; Real-time monitoring of equipment health, production, and safety; Holistic views of reservoirs, wells, and facilities for optimized field development; Efforts to standardize data formats and enhance interoperability; Leveraging cloud platforms and big data analytics for scalability and efficiency and Connecting sensors and devices for enhanced insights.

We address the remaining challenges for full fledge adoption including prioritizing change management, training, and cross-functional collaboration, while fostering a learning culture to adapt to evolving technologies and industry dynamics. This paper contributes novel insights by bridging the gap between DOF and the energy transition. It provides actionable guidance for oil and gas professionals, enhancing their ability to navigate the digital frontier while addressing sustainability goals. By synthesizing existing knowledge, we aim to propel the petroleum industry toward a more efficient and resilient future.
Resolving a Flow Assurance Issue at Offshore Field: A Successful Novel Technology Deployment
Authors: Hendraningrat L. , Rodzali M. A. , Ramly N. A. , Riyanto L.
Published: September 2024

Solid scale like deposition is one of the most common flow assurance challenges, both subsurface and surface. Solid deposition above a certain limit may limit outflow and productivity. An offshore field in Malaysia has suffered mixed scales (wax and inorganic scale), which has decreased oil production. As observed from the well site, the recovered sample was retrieved from the tubing in the wellbore. This paper presents a successful deployment of in-house novel technology to resolve a potential flow assurance issue due to mixed scales in the offshore field.

The mixed scales build-up over time and eventually reduces the effective flow path leading to a flow restriction or eventually a blockage of the line. Mixed scales deposits in tubing reduce the inner diameter and flow rate. Eventually, the wax forms a blockage, prohibiting oil production. Comprehensive analysis and evaluation both in the laboratory and well were carried out, integrated with thermodynamic modelling prior to novel technology deployment in the field.

The loss of ignition test revealed that more than 99% of the deposit is an organic material and the rest is inorganic. PVT fluid data was characterized using the EOS model, and fine-tuned fluid properties achieved an acceptable match with PVT experimental data as a validation process. The comparison of wax content between laboratory measurements and model prediction is acceptably accurate. Laboratory measurements and model predictions match wax content. The in-house chemical treatment was developed and successfully dissolved mixed scales overnight, according to dissolution tests. The compatibility test with existing production chemicals such as scale inhibitor (SI), corrosion inhibitor (CI), and wax inhibitor (WI) was also performed, and the in-house chemical had no incompatibility issues.

This study demonstrates how vital and necessary proper with thermodynamic modelling analysis is prior to well put onstream to anticipate any flow assurance issues that may cause decreased and prohibited oil production. Simultaneously, this paper successfully identified the suitable chemical treatment to control mixed scales in this field.
A Comparative Analysis of Completion and Reservoir Data to Decipher Productivity Drivers in North American Tight and Shale Plays
Authors: Indina V. , Singh H. , Liu Y. , Gu Y. , Song H. , Li C. , Zhang D. Leslie , Kong F. , Cheng P. , Li Z. , Li W.
Published: September 2024

The primary objective of this study is to thoroughly assess the influence of various completion, fracturing stimulation, and intrinsic reservoir properties affecting productivity of ten major unconventional oil and gas plays while uncovering key insights and emerging trends unique to each play.

We examined a dataset comprising of 91,519 horizontal wells that began production on or after January 1st, 2015, across ten major unconventional oil and gas plays (Bakken, Delaware, Duvernay, Midland, Eagle Ford, Scoop|Stack, Haynesville, Marcellus, Montney, and Utica) in North America. The analysis centered on four main aspects: well fracturing, downhole consumables, production, and geological properties. The horizontal and vertical well spacings were based on the horizontal and vertical distance between the target wellbore and its closest near neighbor in any zone, respectively. The wells were classified as parent, child, or co-completed based on the distance and timeframe between their completion. The Bakken-US play shows the most remarkable increase in normalized production per 1000 ft of lateral, almost doubling from 2015 to 2024, while Scoop|Stack experienced a decline in normalized production. Bakken and Montney, with high TOC, had low normalized productivity, whereas Utica, despite low TOC, was highly productive, underscoring completion quality's importance over organic quality. Although high brittleness is usually seen as beneficial for promoting fracturing, it is intriguing to note that plays with fewer wells per pad (e.g., Scoop|Stack) exhibit a higher growth rate of fractures, even when they have a lower brittleness index (median < 0.34). Horizontal spacing plays a crucial role in optimizing performance. Less productive plays benefit from denser development. Co-completed wells outperform other sequencing, indicating their ability to mitigate "frac hits" or fracture-driven interactions. Longer laterals offer better resource contact, but productivity does not increase linearly. Sand remained the predominant proppant used in all plays due to cost-effectiveness. Freshwater remains the dominant frac fluid across all plays, and slickwater frac jobs consistently yield high productivity. The type of chemicals in terms of their dominant use varies across plays, but the trend in each play is largely unchanged since 2015. The number of clusters per stage varies across plays, and increasing the number of clusters may not always lead to enhanced productivity due to the stress shadowing effect.

This study offers important insights into factors affecting productivity in shale plays, aiding future well development and resource extraction optimization. It provides technical guidance for unconventional oil and gas developments in North America and is a useful reference for similar projects globally, including in China.
ConGANergy: A Framework for Engineering Data Augmentation with Application to Solid Particle Erosion
Authors: Zhang Jun , Li Yijie , Pei Weiping , Shirazi Siamack
Published: September 2024

Erosion is a phenomenon involving material removal due to fluid entrained particles impinging on material surfaces. Experiments are crucial to investigate erosion severity and the impact of different physical parameters. However, conducting these experiments is expensive and time-consuming, and collecting high-quality data is challenging due to the small scales of material removal and sensitivity to external environment changes. Over the years, research at the Erosion/Corrosion Research Center (E/CRC) at the University of Tulsa has focused on investigating solid particle erosion in pipelines and fittings. Experiments are conducted using a large-scale boom tower flow loop, and erosion data is collected with high-accuracy, temperature-compensated ultrasonic transducers. This has resulted in more than 200 sets of high-quality experimental data covering various pipe diameters, particle diameters, sand concentrations, and flow velocities. Mining the inherent patterns and physical laws within this data is valuable for guiding future process condition selections and experimental tests. With the existing data covering a wide range of conditions, machine learning methods can be utilized to augment the data, ensuring the newly generated data adheres to physical laws and has similar distributions. Conditional Wasserstein Generative Adversarial Networks (CWGANs) are a class of machine learning models designed for generating conditioned data. CWGANs can be applied to generate additional training data when the available data is limited, significantly expanding the erosion databank for subsequent mechanistic modeling. This work proposes the Conditional GAN Synergized SPPS model (ConGANergy) framework for engineering data augmentation, which utilizes CWGANs to expand erosion experimental datasets. We conducted experiments to evaluate the performance of the proposed framework by comparing the newly generated data against original experimental data and data obtained through a semi-mechanistic erosion prediction model Sand Production Pipe Saver (SPPS) developed at E/CRC. This approach provides engineers and researchers with a powerful tool to expand databases and build robust mechanistic models, leveraging the inherent patterns within high-quality experimental data while adhering to the physical laws governing erosion phenomena.
Laboratory Validation of Microannulus Repair Using Nanosealant Technology: Pre- and Posttreatment Logging and Permeability Characterization
Authors: Kragset S. , Govil A. , Beltrán-Jiménez K. , Jain B. , Gardner D. , Ford E. P. , Taheri A.
Published: September 2024

With aging oil and gas fields, permanent plug-and-abandonment activity is becoming significant and requires solutions capable of repairing leak paths. The use of nanosealant technology to treat defects in annular cement was evaluated using ultrasonic logging and permeability tests on test cells. A repeatable testing procedure was developed to validate the usability of the nanosealants to repair cemented annular barriers.

Two identically constructed test cells were fabricated with 7-in. and 9 5/8-in. casings filled with standard Class G cement. Pressurization of the inner casing during cementing enabled a controlled debonding of the annulus cement and the 7-in. casing upon relieving the pressure after the cement was cured. Permeability tests with water and ultrasonic logging measured the baseline state for the test cells in dry and wet conditions before treatment with a nanosealant solution. After treatment, permeability testing and ultrasonic logging in dry and wet conditions were repeated, and the results were compared with the baseline measurements.

The test cells were shown to have equivalent properties with respect to permeability and debonding and demonstrated along with related experiments that a consistent and repeatable procedure for the qualification of microannulus treatment materials can be followed. The nanosealant was able to penetrate a microannulus with an initial hydraulic aperture of about 50 μm, and the permeability measurements showed that the permeabilities were significantly reduced in both cells after the treatment. Ultrasonic logging using pulse-echo and flexural measurements results indicated improvement in the bond quality. Even though nanosealant has been used successfully in the field, this work represents the first comprehensive laboratory validation of the product supported by logging measurement and provides insight into the nano-sealant activation mechanism.

A procedure for consistent fabrication of test cells for qualification of microannulus treatment materials was demonstrated. A multidisciplinary team developed a methodology that combined physical flow measurements with ultrasonic measurements to validate the performance of a nano-sealant system and confirm the coverage of the microannulus.
Two-Step Process to Quantify Effects of Geological and Completion Parameters on Unconventional Well Performance
Authors: Kelkar M. , Itteyra A.
Published: September 2024

Machine learning algorithms have been frequently used to evaluate the performance of unconventional wells. Typically, all available features (geological and completion) are used to correlate the data to production performance of wells. Unlike the traditional approach, we correlate production data first to geological and geophysical parameters (depth, porosity, permeability, brittleness, etc.). We then take the residual information after the first correlation and correlate the residual to completion parameters such as length, proppant amount and fracturing fluid. By separating geology from completion, we can isolate the effect of geology removed from completion. This is very useful for exploration prospects where very limited or no data are available from existing wells. Further, by correlating completion parameters separately to residual values, we can quantify the impact of each completion parameter which can help optimize completion parameters to get the most benefit of various completion strategies. We use SHAP (Shapley Additive Prediction) to isolate the effects of each completion parameter.

Using large, confidential, data available, we evaluated multiple basins and applied the proposed methodology. These include: Eagleford, and Marcellus Basins. What we observed, in general, is that approximately 70 % of the variance in production data can be explained by geological parameters and about 20 % of the variance can be explained by completion parameters. True vertical depth consistently ranked as the most important geological feature in predicting performance, but other features varied in importance. If appropriate analog can be understood, the methodology from mature formations can be used in exploration blocks to identify sweet spots. The evaluation of completion parameters revealed that the relationship between performance and completion is not always linear and in some instances, incremental benefits of larger completion inputs are negligible and counter-productive. The length of the well is the most important completion variable in determining the performance. The impact of individual parameters varies depending on production parameter selected. Geology becomes increasingly important as more production data are added (12 month vs 24 month vs EUR) and completion parameters are less able to explain the performance as the length of the production data increases.
Active Magnetic Ranging from Bottom Hole Assembly: Tool Performance
Authors: Ytrehus J. D. , Husby K. , Saasen A. , Hjelstuen M. , Eriksen T. J.
Published: September 2024

A well intercept operation has the scope to drill into an existing well. Similarly, some infill drilling operations are conducted with the scope of avoiding interception with other wells. A prototype of a tool for Active Magnetic Ranging While Drilling (AMR) without the use of a wireline operation has been developed for use in petroleum and geothermal wells. The main scope of the current article is presenting the results of application of this new tool in a test well.

The ranging tool emits a low frequency alternating current into the formation to reach the target well and then run through that's casing and returning back to the well being drilled. This current set up a variable magnetic field that is measured by the AMR tool determining the direction towards the target well as well as the distance. If drilling a relief well, 10 - 25 wireline runs are needed before the target well is intercepted. The present tool perform similar operations with an integrated tool systems in the drill pipe and, thus, avoid all the tripping operations.

A prototype of an active magnetic ranging tool on the drill pipe has been developed. This tool is outlined in detail in the paper. Focus will be given to performance of the tool. The tool and the progress have been described in earlier SPE papers. The current article describes the most recent results of the test well performances. The set-up and the results of the logging operation conducted on a drill pipe is described in detail. It is shown how the direction and distance between the two wells are measured using the tool. It is shown by computer simulations how these measurement results can be improved.

Most intercept operations are not relief well drilling, but cases where a well needs to be intercepted because a well section shall be connected to another well, or during plug and abandonment operation. The tool can also be used for avoiding collision with other wells, which is a relevant scope for drilling infill wells in older fields or radiator wells in geothermal drilling. The tool and its potential are outlined in the paper
Transfer Learning in Subsurface Flow Surrogate Model with Physics-Guided Neural Network
Authors: Cheng H. B. , Qiao J. H. , Wei Y. C. , Li S. C. , Zeng P. , Yu H. B.
Published: September 2024

It is a great challenge for reservoir engineers to accurately and quickly model the subsurface flow surrogate for oil and gas reservoirs. The traditional numerical simulation methods are high computational complexity and time-consuming. The widely used pure data- driven flow surrogate methods require massive high-quality data due to lack of theoretical foundation. Therefore, it is necessary to develop transfer learning methods integrating prior knowledge with data-driven deep learning methods to solve complex seepage problems. To reduce data requirements and improving modeling efficiency, a theory-guided and data-driven transfer learning method is proposed to build the fast and accurate subsurface flow surrogate model. Specifically, a data-driven machine learning method is first proposed to simulate and predict the flow processes and build the primary surrogate model. In addition, physical mechanism and constraints are embedded into the data-driven model to make the prediction results satisfy the prior domain knowledge. The transfer learning method based on physics-guided neural network (TL-PG) integrates the seepage theory with sparse spatial data to improve the prediction accuracy of the surrogate model. The proposed TL-PG method is verified by a subsurface flow problem in heterogeneous reservoir models. First, we build a flow field to simulate 50 years of production history. A physics-guided neural network model is trained based on the samples from the first 30 years and tested based on the last 20 years of data. The relative L2 loss and the coefficient of determination R2 are used for comparison. Compared with the purely data-driven method, the relative L2 loss of physics-guided neural network is increased by 16%, and the mean coefficient of determination R2 is up to 0.8932, which means the embedded physical mechanism can greatly improve the performance of neural networks. Secondly, the physics-guided neural network is selected as the pre-training model. Then, the random points from the year 31 to 40 are used to fine-tune the model. Finally, the TL-PG surrogate model is validated by the last 10 years of data. The mean coefficient of determination R2 is up to 0.9522, which has improved by 25% compared to purely data-driven deep neural network (DNN) method, and only when the number of training data and epochs are increased several times, the performance can be comparable to the TL-PG method. However, the training time of DNN is more than 3 times that of the proposed method. The results show that the transfer learning surrogate model can effectively capture the feature of subsurface flow fields and greatly reduce the demand for data while significantly increasing computational speed.
An Automated Workflow for Condition Monitoring of Centrifugal Compressors Using a Combined Data-Driven and Physics-Based Approach
Authors: Santiago C. J. S. , Abbas H. , Thangamani P.
Published: September 2024

Equipment degradation is inevitable during operation. The breakdown of equipment is costly due to replacement expenses and may cause a facility to shut down, leading to higher costs and production losses. Hence, equipment maintenance is essential to ensure a satisfactory level of reliability during its lifecycle. Maintenance strategies may adopt a reactive approach, in which maintenance happens after failure and should be avoided, or a preventive approach, in which maintenance happens regardless of the state of the system.

This work aims at addressing the challenges posed by system deterioration and costly downtime through the development and implementation of a condition monitoring solution for a centrifugal compressor situated in an offshore platform. To achieve this objective, a comprehensive methodology was developed containing two modules: fault detection and prognostics. In the fault detection module, a hybrid approach was adopted by combining measured and simulated data generated using physics-based models, where a machine learning model is trained to detect anomalies and generate alarms. In the prognostics module, a degradation state variable is introduced to predict the time to failure of the centrifugal compressor.

By leveraging process simulation to construct a physics-based model, the system can track changes in design polytropic efficiency using real-time sensor data to make predictions of time to failure using a Kalman filter algorithm. We demonstrate the methodology through examples using actual centrifugal compressor measurements collected from Valhall public industrial data. The fully automated solution provides real-time insights on the operational state of the centrifugal compressor, enabling proactive maintenance strategies. The analysis of centrifugal compressor sensor data is streamlined to detect anomalies and trigger alarms for different sensors, enabling prediction of time to failure within a few days before the compressor shutdown happens.

The approach presented in this work integrates the strengths of both physics-based and data-driven methods. The methodology exploits the alarm generation and complements it with a time-to-failure prediction using a Kalman filter algorithm. The presented solution contains a real-world application of machine learning in condition monitoring and has the potential to enhance predictive maintenance planning and prevent costs associated with downtime.
Predicting Permeability in Real-Time from LWD Resistivity and Gamma Ray Logs
Authors: Norbisrath J. H. , Sangolt V. , Russell A. K.
Published: September 2024

Real-time permeability prediction and flow risk assessment is useful both for the safety of drilling operations as well as operational challenges like determination of pressure point depths and perforation zones. A model giving accurate permeability predictions based solely on LWD (logging-while-drilling) data can potentially save days of rig time by eliminating the need for wireline or coring runs. Drilling safety profits from early detection of flow zones that can destabilize the wellbore and require timely treatment. Our goal is to implement these predictive models into real-time geo-operational drilling software which can then be used by geoscientists and engineers alike.

The novelty of this study is the large amount of available training data. We build upon ongoing efforts to lift both legacy and newly acquired petrophysical log and core data to the cloud, creating a training dataset on a continent-wide scale. The core data contains about 650,000 core permeability measurements, around 43,000 of which have corresponding (on-depth) logging-while-drilling (LWD) resistivity propagation and gamma ray curves. The combined dataset is mainly based on wells from the NCS (Norwegian Continental Shelf). This extensive training dataset is vital for developing universally applicable models across different formations of the various basins on the NCS. Nonetheless, since the data is from various vintages and vendors, a large part of the work evolves around data preparation, cleaning, and normalization. Standard machine learning techniques are then used to train the model and assess its predictability on new data from unseen wells.

Results show that a model that uses just LWD gamma ray and resistivity curves as input can predict unseen data in new wells with a correlation coefficient of r = 0.70. Since the input logs are ubiquitous in basically any hole size, the model can be used to both identify shallow hazards (flow zones) as well as permeable target intervals in the reservoir. Adding in neutron and density curves, typical for reservoir section drilling, the model’s accuracy increases to r = 0.85.
Field Application of Image Analysis Models to Measure the Drill Cuttings Recovery Rate
Authors: Gosavi Shekhar , Gilroy John , Ruel Francois , Holt Calvin
Published: September 2024

This paper presents the development, rigorous validation, and practical application of an image analysis model combined with a remotely monitored and autonomous camera system to measure the recovery rate of drilled cuttings. The primary objective was to assess the effectiveness of hole cleaning, a crucial factor in preventing well construction issues. The model's reliability and accuracy were successfully demonstrated during the continuous monitoring of five wells in the Permian Basin.

An optical sensor system with onboard image analysis software was installed inside the hazardous zone near the shale shaker and used to monitor and analyze returning drill cuttings while drilling 6.75-inch lateral hole sections. The varying cutting loads were measured quantitatively and qualitatively using computer vision techniques, including optical flow. The images were analyzed onsite, and data on actual and estimated shaker loads was delivered. The load was measured on only one of three shakers. The project was managed remotely using a multi-platform, open-source analytics and interactive visualization web application.

Before deployment, the image analysis model was calibrated and verified against an experimentally validated cuttings transport model by performing experiments at different cutting load rates, volumes, and fluid types using a drilling research flow loop. This calibrated model was deployed during drilling to measure the cuttings recovery rate (CRR) on a relative basis (0-10) and an actual basis (barrels per hour).

Approximately 500 bbl. of drilled cuttings were optically measured and compared with expected volumes. The relative cuttings load demonstrated a credible correlation during controlled, lower rate of penetration (ROP) and sweep circulation periods. The actual CRR showed logical results during daylight periods. Additional observations are reported concerning wet versus dry shaker conditions, performance under low light conditions, and the identification of potential time savings based on measured shaker loads.

Implementing an image analysis model to analyze drill cuttings in real-time—including autonomously characterizing a drilled cutting’s size, shape, and volume—represents a significant leap toward drilling faster and reducing risk. This promising technology has the potential to significantly reduce the non-productive time associated with circulating and mitigating pack-off events, effectively addressing inadequacies in current cuttings transport modeling and hole-cleaning practices. The experimental validation work and field examples reported here demonstrate the progress towards applying newly developed image analysis capabilities to successfully measure drill cuttings recovery rates, underscoring the importance of this research in the oil and gas industry.
Extracting Petrophysical Properties from High Frequency Drilling Vibration for Geosteering in Challenging Environments
Authors: Al-Ofi Salah M. , Ma S. Mark , Ali Shujath S. , Maity Sandip
Published: September 2024

Drilling vibration data is traditionally used for optimizing drilling operations. Recently, we showed that high frequency drilling vibration data has the potential in identifying lithology and changes in formation properties for qualitative geo-steering. In this paper, we will present in detailed investigations extending previous findings to various drilling conditions and environments.

We have explored drilling vibration datasets obtained from triaxial accelerometer measurement module from different drilling environments, with different drilling parameters, borehole sizes, and placement of accelerometer module from the bit. Beside the typical accelerometer measurement sampled every 5 seconds, we configured the accelerometer recording to capture axial, lateral and tangential vibrations at 2,500 Hz sampling rate to record up to 1,250 Hz accelerometer vibration. These high frequency vibration modes were processed in frequency domain by use of Fast Fourier Transform (FFT) and analyzed, in terms of frequency and amplitude, against other drilling parameters such as weight and torque on bit. Insight from the analytical model correlated against formation properties derived from LWD logs.

Frequency spectrum showed qualitative correlation between axial vibration amplitudes and formation properties using real-time averaged data. Different correlation trends may exist, but generally axial vibration amplitude is inversely proportional to neutron porosity and directly proportional to bulk density. Significant correlations have been found between vibration peaks at frequencies higher than 500 Hz and formation neutron porosity and bulk density. The characteristics vibration frequency peaks may vary depending on drilling parameters used, borehole conditions and accelerometer offset from drill-bit.

The findings of this study further confirm our previous work and open a path for advanced geosteering in complex drilling conditions where bottom hole assembly is restricted such as in slim hole under balanced coil tubing drilling, or when LWD sensors are not available.
Estimating Rock Typing in Uncored Wells Using Machine Learning Techniques for Brazilian Pre-Salt Carbonate Reservoir
Authors: Allahham Mohammad Saad , Botechia Vinicius Eduardo , Davólio Alessandra , Schiozer Denis José
Published: September 2024

Accurate rock typing in uncored wells is essential for enhancing reservoir models, particularly in complex geological formations like the Brazilian pre-salt carbonate reservoirs. This study explores the application of machine learning (ML) techniques to estimate rock types in uncored wells. The research leveraged core data from 11 cored wells to calculate the Rock Quality Index (RQI) and Flow Zone Indicator (FZI), identifying 14 distinct rock types through the Discrete Rock Typing (DRT) method, along with well log data such as gamma ray, density, neutron, permeability, and porosity. Various machine learning models, including Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree, Gradient Boosting, Naive Bayes, XGBoost, and Random Forest were tested, where XGBoost achieved the highest accuracy of 73.3%. Applying XGBoost to all wells resulted in accuracy ranging from 0.6 to 0.91 and the model was subsequently used to estimate rock types in over 20 uncored wells to generate reservoir simulation models. This study highlights the efficacy of machine learning (particularly XGBoost) in addressing reservoir complexities and offering significant improvements in the understanding and development of carbonate reservoirs.
Refined Diffusion Posterior Sampling for Seismic Denoising and Interpolation
Authors: Nguyen Tung , Pham Tung , Nguyen Minh , Nguyen Tuan
Published: September 2024

Seismic data is vital in the petroleum industry, revealing subsurface structures and reservoir potential. However, this data frequently suffers from degradation due to noise and missing traces, necessitating denoising and interpolation tasks. Recently, diffusion models have gained prominence, showing remarkable results in diverse image enhancement tasks. In this paper, we explore several cutting-edged diffusion-based techniques for addressing seismic denoising and interpolation challenges. Furthermore, we propose a novel method, which addresses the limitations of existing approaches, establishing a new state-of-the-art in the field, offering enhanced accuracy and robustness in seismic data processing.
Measuring Hydrogen Permeability of Salt Rock for Applications in Energy Storage
Authors: Yuan Lin , Najafimarghmaleki Adel , Dehghanpour Hassan
Published: September 2024

This study investigates the hydrogen permeability of salt-rock plugs from the Lotsberg Salt Formation and its caprock, the Ernestina Formation. We employed a newly-developed pressure pulse decay (PPD) system, which is modified and extended from the existing systems for shale-rock characterization. A series of experiments were conducted on each salt rock to measure hydrogen permeability across an average pressure range of 100 to 1000 psi. The results highlight the significant impact of the Klinkenberg effect, where hydrogen slippage leads to an overestimation of measured permeability values. After correcting for this effect, the absolute permeabilities to hydrogen were determined to be 0.313 nD and 4.32 nD for the salt rocks from Ernestina and Lotsberg formations respectively. The interface between the grains of the Lotsberg marlstone provides hydrogen flow pathways, thereby increasing its permeability. The sample from Ernestina caprock, primarily composed of 92% anhydrite with minor dolomite and halite, has fewer crystal boundaries and grain interfaces, resulting in lower permeability.
Investigating Fracture Growth After Shut-In
Authors: Zhang Qitao , Taleghani Arash Dahi
Published: September 2024

Hydraulic fractures are often assumed to immediately stop growing once pumps are shut off after fracturing treatments. However, fluid momentum and rock properties may allow propagation to continue temporarily post-shut-in. The objective of this study is to determine if and under what circumstances fractures actually arrest after pump shut-in through numerical modeling. To address this gap, an integrated numerical model is proposed to simulate the fracture propagation process and shut-in process, which coupling the fluid flow, geomechanics, and fracture mechanics. A mathematical model estimates fracture length post-shut-in based on parameters like injection rate and fluid properties. This multifaceted modeling approach provides insights into fracture growth mechanisms once pumps are shut-in. Modeling shows fractures may continue propagating after pumps are shut-in, significantly influenced by injection rates, fluid/rock properties, and geometry. Leveraging the presented mathematical model, we can roughly see how different factors can affect the movement of the tip of a tensile fracture. Simulation results show that the presence of post-shut-in fracture propagation can lead to an 9.17% increase in fracture half-length under the model conditions. Besides, higher fluid momentum and smaller fracture volume can support the continuation of fracture propagation after the pumps are turned off. A fast and short fluid injection (injection rate and time are 0.0008 m3/s and 200 s) can lead to an 11.4% increase in fracture half-length. Then, it was also found that lower leak-off coefficients can also help improve post-shut-in fracture propagation by up to 10.18%. Neglecting this post-shut-in effect in diagnostic fracture injection tests (DFIT) could misestimate stress and rock properties. This research provides new insights on fracture growth mechanisms especially for post-shut-in, enabling better fracture control and test interpretation. This study uses numerical modeling to determine if/when fractures stop after shut-in. Results give a better understanding of post-shut-in fracture growth, improving fracture control and test interpretation.
Negative Testing: Automating a Safety Critical Process with Human Factors at its Core
Authors: Bruce C. , Baumgartner T. , Liu Y. , Ali H. , Smith T. , Griffith S. , Kirker C. , Garrett J. , Murphy D. , Tonagel V. , Adeleye O.
Published: September 2024

Pressure testing in the oil and gas industry is used to validate the safety and integrity of equipment across a well barrier. Negative testing, also known as inflow testing, requires a negative pressure differential between the well and the formation. This type of pressure testing is used in well systems to test the integrity of a well barrier envelop. The most infamous negative test in oil and gas history was the test on the Macondo well, shortly before the tragic incident unfolded.

Current practices vary across the globe, they depend on regional regulations, industry standards, location, and company procedures. The two basic types of approaches are based on analysing volume or pressure data. The most common methods are no-flow, pressure monitoring, and Horner. Volume typically is measured using tanks, buckets, and beakers. Pressure traditionally was measured using an analogue pressure chart recorder, which now is increasingly being replaced by real-time data systems. As the oil and gas industry moves towards digital systems for pressure testing, the need for a systematic methodology with clearly defined criteria for negative pressure testing becomes ever more important. Computers cannot interpret ambiguity, so it is essential that a systematic methodology for negative pressure test acceptance is clearly defined, and criteria are developed.

This paper proposes a systematic approach aimed at enhancing the interpretation and standardisation of negative pressure testing results with human factors at its core. Drawing from existing literature and an analysis of over 100 negative tests conducted by the operator, seven key principles are established. These principles serve as a unified framework for interpreting negative test results and provide the much-needed detail for automating the acceptance process. Through their application in diverse testing scenarios in deepwater environments, this work identifies potential inconsistencies in the current methodologies and proposes strategic adjustments. The aim is to foster a more robust and standardised approach to negative pressure test interpretation, enhancing safety, compliance, and operational efficiency in the oil and gas industry.
Practical Applications of Space-Time Fractional-Diffusion in Naturally Fractured Reservoirs
Authors: Chen Chih , Chu Wei-Chun , Bermudez Leonardo
Published: September 2024

This study introduces a radial space-time fractional-diffusion (ST-FD) model, now adapted for Laplace space. This model incorporates dual porosity factors and, when applied to well tests in naturally fractured reservoirs, addresses the effects of rock heterogeneity in fractured or fissured formations by solving a multi-index ST-FD equation through the Laplace transformation. For the first time, pressure distributions in cylindrical coordinates for heterogeneous fractured rocks with space and time-dependent properties are presented. These findings are novel and unreported in existing literature, highlighting the utility of this approach in analyzing fractured reservoirs. The method developed in this paper was applied to five example well tests from fractured carbonate fields in Northern Iraq, demonstrating the practical application of this methodology.

Two key models commonly used in the industry to study naturally fractured reservoirs are the Warren and Root (1963) dual porosity model and de Swaan's (1976) model. The Warren and Root model assumes a pseudo-steady-state flow between the matrix and fractures, while de Swaan's model addresses transient flow between these components. When applied to pressure buildup responses in fractured reservoirs from Northern Iraq that exhibit a power-law pattern, these traditional models could lead to misinterpretations, attributing the behavior to boundary effects such as faults.

In contrast, employing the Continuous Time Random Walk (CTRW) framework, specifically the space-time fractional-diffusion (ST-FD) model developed by Raghavan and Chen (2021, 2023), and now modified in this paper with solutions in Laplace space, provides a more accurate interpretation of these pressure responses. The analysis of real-world data using this modified ST-FD model reveals that the power-law behavior in pressure buildup responses is linked to the reservoir's complex heterogeneity as shown in five example well tests presented here. The effectiveness of the modified ST-FD model in Laplace space is validated, offering practical insights for well testing and accurate evaluations of reservoir recovery potential. Although the system is intricate at a fine scale, the results are applicable in the conventional sense at the Theis (1935) scale. Long-range connectivity and distorted flow paths that result in pauses, flow reversals, and related phenomena are quantified through three exponents and fractional derivatives.

The findings highlight the influence of diverse heterogeneity indexes and introduce an effective workflow for interpretation and reservoir assessment. These insights significantly enhance comprehension of the reservoir's complex heterogeneity, leading to better estimations of Ultimate Recovery (EUR).
Hyperspectral Image Segmentation of Methane Plumes Using Large Machine Learning Model
Authors: Zi Yuan , Chen Jiefu , Wu Xuqing , Fan Lei , Han Zhu
Published: September 2024

This paper presents a hybrid model for methane plume detection, merging traditional filters with a large computer vision model for efficient monitoring of large-scale emissions. It swiftly identifies methane emission plumes, overcoming false alarms and manual inspection issues. Compared to the previous methods, results show that the new method enhances scalable methane detection while minimizing data acquisition and training efforts.

The new approach integrates a swift-matched filter and a robust computer vision model. The initial matched filter outcomes, derived from image processing, are utilized as customized prompts for the Segment Anything Model (SAM), a potent segmentation system. SAM, renowned for its zero-shot generalization capabilities, efficiently identifies methane plume objects and images without requiring additional training. This eliminates the necessity for extra training efforts, streamlining the process of scalable methane emission detection for enhanced efficiency in monitoring large-scale emissions through hyperspectral imagery.

The proposed hybrid model undergoes comparison with the baseline matched-filter model published previously. Experimental studies are conducted using the NASA JPL Benchmark for Methane Source Detection from Imaging Spectrometer Data, involving 54 spectrometer images taken from a 3km high altitude with 161 CH4 plume sites. The new model's accuracy and detection of false alarms are assessed against matched filters. A significant discovery is the large machine learning model's ability to zero-shot generalize for segmenting methane plume objects with minimal false alarms, requiring no additional training on methane data. This is crucial for automating large-scale processing, avoiding manual intervention necessitated by previous high false alarm rates. Additionally, the absence of training requirements is noteworthy due to the computational cost and data imbalance issues associated with traditional anomaly detection machine learning procedures, where methane data is a small fraction amid predominantly background data.

The novelty of the new model lies in its ability to solve methane plume segmentation efficiently with high performance, achieved through prompt engineering and the utilization of a large machine-learning model.