bluuebunny/crossref_metadata_2025_split huggingface dataset:
Predicting Permeability in Real-Time from LWD Resistivity and Gamma Ray Logs
Authors: Norbisrath J. H. , Sangolt V. , Russell A. K. 
Published: September 2024

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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.