bluuebunny/crossref_metadata_2025_split huggingface dataset:
Structural Dip Estimation from Crossbedding on Borehole Images
Authors: Sultan Gamal , Jibreel Walid 
Published: April 2019

Abstract
Structural dip is the term used in borehole image and dipmeter interpretation to indicate the "tectonic" tilting in the vicinity of the wellbore. Structural dip, by definition, is the formation dip component that is caused by tectonic deformation such as folding, faulting, uplift and others.
Knowledge of the structural dip in the vicinity of the borehole is essential for several applications, including field structural modeling, well placement, geosteering of the lateral sections, and seismic data processing.
Traditionally, structural dip is computed from borehole image data using laminated shale dip based on the assumption that the laminated shale was deposited out of suspension and that the lamination was originally deposited as horizontal beds. This means that any tilting observed in laminated shale with "coherent" lamination is caused by tectonic tilting; hence, it can be used to compute the structural dip. There is nearly a consensus in the industry around this assumption, and the laminated shale dip is widely used to compute structural dip.
There are several geological settings under which laminated shale can form. Those are mostly subaqueous setting such as marine and lacustrine settings. Drilling through rocks deposited in such settings normally encounters sequences of laminated shale from which structural dip can be computed. However, rock formations deposited in subaerial environments often lacks settings under which laminated shale forms. Such environments are often dominated by sandstone lithologies deposited in high- energy settings this rich in sedimentary structures such as crossbedding. Due to absence of laminated shale sequences, computation of structural dip using the traditional approach is not possible.
This paper explains a technique that can be used to estimate structural dip from cross bedding on borehole images. It uses the geometrical relationship between the crossbedding surfaces and the lower set boundary of the corresponding crossbedding set. The line of intersection between these two surfaces is assumed to be horizontal at the time of deposition. Measuring multiple lines of intersections, plotting them on a stereonet, and fitting a great circle to them helps estimate the structural dip within the analyzed interval. The best- fitting great circle of these lines is believed to be a reasonable estimation of the structural dip.
This approach has been tested on few image log datasets with cross bedded sandstone facies and proved to be very close to the actual structural dip computation obtained from the shale facies in the same depositional sequence. This paper will illustrate some interpreted image log supporting this technique.
Data Mining Approaches for Casing Failure Prediction and Prevention
Authors: Noshi Christine , Noynaert Samuel , Schubert Jerome 
Published: April 2019

Recent casing failures in the Granite Wash play in the western Anadarko Basin have sparked deep concerns to operators in North Texas and Oklahoma. Hydrostatic tests made in the field show that present API standards do not assure adequate joint and bursting strength to meet deep-well requirements. Past and present literature has been infested with numerous casing failures incidents. Despite the extensive documentation and recommendations, a mounting trend of failure is still on the rise. In an attempt to find possible solutions for these failures, this study is a continuation of an on-going effort to minimize the likelihood of failure using Data Mining and Machine Learning (ML) algorithms.
The study applied both descriptive visual representations such as Mosaic and Box Plots and predictive algorithms including Artificial Neural Networks (ANN) and Boosted Ensemble trees on eighty land-based wells, of which twenty possessed casing and tubing failures. The study used a predictive analytics software and python coding to evaluate twenty-six different features compiled from drilling, fracturing, and geologic data.
This work attempts to shed light on operational problems and implement a Data Analytic approach to find out the possible factors contributing to casing failures using both descriptive and supervised ML algorithms.
The Embedded VGG-Net Video Stream Processing Framework for Real-Time Classification of Cutting Volume at Shale Shaker
Authors: Du Xunsheng , Jin Yuchen , Wu Xuqing , Liu Yu , Wu Xianping , Awan Omar , Roth Joey , See Kwee Choong , Tognini Nicolas , Chen Jiefu , Han Zhu 
Published: April 2019

Abstract
A deep learning framework is proposed and implemented for monitoring the cutting volumes of a shaker in real-time on a deep-water drilling rig. The framework aims at performing classification and quantification with a live video streaming. Compared to the traditional video analytics method that is time-consuming, the proposed framework is more efficient and can be implemented for a real-time video analysis application. The real-time deep learning video analysis model consists of two parts for processing. The first part is a multi-thread video processing engine. A modularized service named Rig-Site Virtual Presence (RSVP) provides real-time video streaming from the rig. The multi-thread video processing engine implements real-time decoding, preprocessing and encoding of the video stream. The second part is a customized deep classification model. Based on the deep neural network (DNN), we implement the following adaptations: 1) Applied whitening and instance normalization to video frames; 2) Optimized the number of convolutional layers and the number of nodes in fully-connected layers; 3) Applied L2-norm regularization. The customized model is embedded in the multi-thread video processing engine, which ensures the capability for the real-time inference. The deep learning model categorizes every video frame into "ExtraHeavy", "Heavy", "Light" or "None". The model also outputs the corresponding numerical probabilities of each outcome. The training of the model is accomplished on a Nvidia GeForce 1070 GPU using the video stream with 137Kbps bitrate, 5.84 frames/s, and a frame size of 720×486. With only a common CPU support, the inference of the pre-trained model can be conducted in real-time. Both labeled frames and numerical results will be saved for later examination. Compared to the manual labeling results, the proposed deep learning framework achieves very promising results for analyzing video streaming in real-time.
The Application of High Frequency Resonance Based on MPD in the Identification of Oil and Water Layer
Authors: Zhang Pingping , Hou Dongjia , Ma Xugang , Wang Yichuan 
Published: April 2019

Abstract
How to effectively identify the oil and water layer has been a difficult problem in the hydrocarbon indicator (HCI). It is generally believed that the oil layer has the characteristics of low frequency enhancement and high frequency attenuation on the frequency spectrum. But in the actual application, the amplitude and frequency characteristics of the thicker water layer are very similar to that of the oil layer, which makes it hard to distinguish one from the other. In this paper, in order to identify the oil and water layer, a new method basis on matching pursuit decomposition (MPD) is proposed. Firstly, the time-frequency analysis of seismic data is carried out though high precision MPD method. Through analyzing the instantaneous amplitude at different frequencies, we consider that the main difference of the oil and water layer in the frequency spectrum is at the high frequency band where the oil layer shows relatively strong amplitude characteristics. Secondly, base on the high frequency resonance (HFR), the high frequency bright spot attribute is calculated from the frequency division data in the high frequency range. In this new attribute, the water layer is suppressed by the strong amplitude of the oil layer. Finally, the results of the HCI are obtained by multiplying the new attribute with the −90 degree phase shift of the seismic data. The forward modeling test and actual application in Bohai oilfield show that the high frequency bright spot method is more effective in suppressing the water layer and identifying thinner oil layers compared with the conventional low frequency and high frequency attenuation methods of HCI.
Comparative Analysis of Machine Learning Based Feature Selection Approach for Carbonate Reservoir Cementation Factor Prediction
Authors: Anifowose Fatai , Ayadiuno Christopher , Rashedan Faisal 
Published: April 2019

Abstract
A key component of the fourth industrial revolution is data integration. However, this comes with a major challenge: handling increased input feature dimensionality. Multivariate feature space increases model complexity, memory utilization, and computational intensity, thereby reducing model performance. A pragmatic approach to input feature space reduction is therefore required. This paper presents a comparative study of the performance of a nonlinear feature selection methodology based on fuzzy ranking (FR). The FR algorithm is extracted from a segment of Fuzzy Logic, an existing machine learning technique. The performance of this feature selection algorithm is tested and validated with respect to the prediction of cementation factor as a log from wireline measurements using machine learning techniques. Cementation factor is denoted by the exponent m in Archie's equation. A subset of the log data selected by the FR algorithm is automatically fed into artificial neural network (ANN) and support vector machine (SVM) models to build FR-ANN and FR-SVM hybrid learning models. A multivariate linear regression (MLR) model is also implemented. The performance of the hybrid models is compared to those of MLR, ANN and SVM without the feature selection procedure. We further compare the outcome with ANN and SVM fed with linearly correlated input features. The hybrid learning methodology is driven by patterns discovered in the data and eliminates subjective human bias in the choice of the input features. It also takes into consideration the possible nonlinear relationship between the wireline logs and m. The FR-ANN model shows improved performance over the other models with the highest correlation coefficient and lowest root mean squared error. The performance of the FR-ANN hybrid model demonstrates the efficiency of the proposed nonlinear feature selection hybrid methodology. A future work will apply this approach to high dimensional, integrated data types from many wells. We expect that the outcome will significantly improve the prediction accuracy and further impact reservoir models using the predicted properties.
Advanced Meandering Fluvial Reservoir Characterisation for Static Model Improvement
Authors: Ismail Hasnol Hady , Lew Chean Lin , Mohamad Som Muhd Rapi , Abdul Kadir Mohd Fauzi , Ahmad Tajuddin Mohamad Raisuddin 
Published: April 2019

Abstract
Modelling of meandering fluvial reservoirs with point bars and crevasse splays is very challenging. The conventional modelling approaches, especially for meandering fluvial reservoirs, are mainly controlled by wells, which have contributed to uncertainties in lateral variations between and away from well control. Integration of the improved sedimentology, geophysics and 3D reservoir geomodelling techniques of fluvial reservoir system are proposed in the study. In stratigraphic and structural framework building, the improved methodologies included 3D seismic geobody extraction, stratal slicing and high order architectural elements interpretation. 3D geobody extraction and stratal slicing technique enhanced interpreter ability to visualize fluvial features at specific time-equivalent stratigraphic surface. In lithofacies modelling, more refined high-order architectural elements were modelled using methodologies included 3D facies seismic probability, local varying azimuth and dip angle to capture lateral accretion of point bars inside the channels for better facies distributions following point bar architectures. In property modelling, porosity was populated using Gaussian Random Function Simulation constraint to lithofacies trend to control the distribution of porosity away from wells. This methodology resulted in the porosity distributions being well controlled following the lithofacies trend. The proposed workflows and methodologies enable geomodeller to produce a more geological realistic meandering fluvial reservoir model with internal lithofacies and property distribution honouring well data and input distribution.
Integration of Electrical Micro-Image Log, Advance 3D Acoustic Anisotropy and Fracture Stability Analysis – A Complete Solution for Basement Reservoir
Authors: Nandi Anindya , Sarkar Subhadeep , Chatterjee Chandreyi , Das Sourav , Pattanaik Sambit , Majumder Chandan , Haldia Bhopal Kumar , Chaturvedi Praveen Chandra , Srivastava Siddharth , Verma Malay , Sarkar Sutanu 
Published: April 2019

Abstract
Owing to the depleting reserves in the conventional reservoirs over the last few years, unconventional reservoirs have gained significant importance in the exploration of oil and gas. Basement rocks, though non-sedimentary in origin, is looked upto as one of the important unconventional reservoirs. Deccan volcanics in Kutch-Saurashtra is one such example from India. This study shows and validates a methodology of how acoustic log data can be integrated with borehole images to understand reservoir properties that governs flow. It has been noticed that presence of open fractures is not the single biggest driver contributing to production. Insitu stress plays a critical role in guiding fracture mobility. To understand and determine which fractures would contribute to flow, a geomechanical study of performing the fracture stability analysis has been carried out. This generates a Mohr circle plot that defines the Mohr-Colomb shear failure criteria using the stress and critical fracture angles. Combining these three-way approaches of acoustic, image log and geomechanics, a workflow has been established for this field to identify fractures and quantify the permeable zones. This workflow has been used for two nearby wells in this field and subsequent result emphasises the utility of this method to find out sweet spots of fluid flow in fractured basement.
Evaluation of Sandstone Stimulation using Thermochemical Fluid Through Distributed Temperature Sensing and CT Real-Time Downhole Flow Measurement Tool
Authors: Al-Nakhli Ayman , Bataweel Mohammed , Arifin Mohammed , Ahmed Danish 
Published: April 2019

Abstract
Distributed Temperature Sensing (DTS) was used to evaluate a novel sandstone stimulation treatment based on exothermic reaction. The technology is based on injecting in-situ heat generating fluid (IHGF) that generate in-situ heat i.e., ~&gt;400F and nitrogen pressure to mobilize and remove near well bore damage in sandstone formations. Distributed temperature sensing (DTS) in real-time was used for fluid placement, treatment optimization and evaluation. Coiled Tubing (CT) real-time downhole flow measurement tool was used to conduct injection profiling before and after pumping the IHGF.
Application of exothermic reaction downhole is very challenging as generated pressure can exceed will head pressure, and generated heat can exceed well completion rating. To have advanced assessment of the technology, generated temperature and pressure increase was captured in real-time with distributed temperature sensing (DTS) coiled tubing telemetry. Hence, CT with fiber optic real-time telemetry was used to pump the IHGF in two injection wells to increase their injectivity. These real-time measurements provided the benefits of recording DTS and enabled recording the injection profile through its real-time downhole flow measurement tool.
DTS and the CT real-time downhole flow measurement tool was used to acquire initial and post-treatment injection profiling of two water injection wells. The results were used in conjunction to optimize the IHGF placement. DTS was also conducted during and after pumping the IHGF to understand the heat generation in real-time and optimizing the treatment as needed. After the treatment, the injection profiling was conducted to evaluate the treatment.
The paper describe the application of CT with fiber optic real-time telemetry for unique treatment involving exothermic reaction for sandstone stimulation. The successful treatment will positively impact sandstone stimulation in oil and gas industry. Results will enlighten the heat generation through IHGF resulted in removing the nearby well damage, followed by increase in injectivity and the effect of real-time measurements have in better treatment execution and evaluation.
Control Effects of Huizhou Depression Tectonic Evolution on the Paleogene Formation Sedimentary Differences
Authors: Cai Junjie , Wen Huahua , Gao Xiang , Cai Guofu , Hu Kun 
Published: April 2019

Abstract
Huizhou Depression is in the exploration peak stage at present. The main target layer is gradually extending from the middle-shallow traps to the deep paleogene traps and the shallow lithologic traps, and the difficulty of exploration is totally increased. Paleogene layer oil&gas exploration is faced with the problems of deep buried depth, reservoir heterogeneity and uncertain distribution of high-quality hydrocarbon sources.
By combining tectonic evolution analysis with sequence stratigraphy, considering regional stress background and the utilizing of the seismic facies, the main faults tectonic features, stratigraphic sedimentary characteristics, the distribution position of sedimentary center and the control effect of the palaeogeomorphology on the sedimentary distribution range deposited from the transition zone are analyzed.
It is concluded that the lower Wenchang period's tectonic movement was dominated by the southern depression control fault, and the semideep-deep lacustrine high-quality hydrocarbon source rocks were mainly distributed in the south of the Huizhou Depression, such as HZ 26 Sag and the subsag of the XJ30 Sag. The braided river delta deposited from XJ30 transfer zone is mainly distributed along the west side of the long axis of XJ30 sag, and the semideep-deep lacustrine facies mudstone is formed in the east of XJ30 Sag. In the upper Wenchang period, the activity of the depression control faults in the northwest of the Huizhou Depression becomes stronger than the south, which influences the sedimentary center migrated from southeast to the northwest. The sediment provenance of XJ30 transfer zone deposits perpendicular to the long axis of the XJ30, and the long braided river delta is formed in the south side of the XJ24 Sag. In Enping period, which is changed from strong rift phase to rift-depression transition phase, the shallow lacustrine-swamp facies are taken as the main source rocks, and shallow braided river delta is widely developed, while the sediment from the provenance of XJ30 transfer zone is weakened.
The northern and southern migration of the transfer zone provenance river delta and the northern and southern distribution characteristics of the source rocks of semideep-deep lacustrine facies are caused by the differences of the northern and southern fault activities during the Paleogene period. Through the combination of structural evolution analysis and sedimentary characteristics analysis, the analysis of paleogeomorphology's effect on the control of sedimentary system is of great importance to the identification of high-quality paleogene reservoirs and hydrocarbon sources.
Behavior and Properties of Cement-Embedded Fiber Optic Sensors for Zonal Isolation Monitoring
Authors: Wu Qian , Nair Sriramya , van Oort Eric , Guzik Artur , Kishida Kinzo 
Published: April 2019

Abstract
A good cement-casing bond is essential for effective zonal isolation in both active and abandoned wells. A new method was developed to monitor the cement-casing bond in real-time and in-situ using a fiber optic distributed temperature and strain sensing (DTSS) system. To demonstrate the concept, the DTSS system was used in a laboratory-scale well model, which has a fiber optic cable installed helically on the outside surface of a steel pipe that served as a model for a casing string. A cement annulus was created by placing the steel pipe with the optical fiber into a larger PVC pipe. The DTSS system successfully captured strain changes at the cement-casing bond due to an axial load applied on the casing. The helical wrapping installation enabled circumferential measurements of temperature and strain changes in the entire cement annulus. The results were used to evaluate the risk of cement debonding by estimating the shear stress in the fiber and by comparing it to the shear strength of the cement bond. In addition, the effect of embedding a fiber optic cable on the hydraulic integrity of cement annulus was also evaluated using a gas permeability test. The permeability of cement samples with embedded fibers was found to be elevated compared to plain cement samples without fibers, but the permeability values were well within accepted industry limits. Compared to existing cement bond logging tools, the proposed fiber optic sensing system can provide continuous, real-time and in-situ monitoring of the cement bond and zonal isolation in either active or abandoned wells, without the need for wellbore entry. The system can serve as an early warning system to identify, and possibly prevent, the loss of a cement barrier, by providing detailed information (i.e. location, type, and severity of an event(s)) that will facilitate any remedial operations, if necessary.
Statistical Model Updates for Fast-Tracked Model Insights and Value-of-Information
Authors: Hardy Madeline , Baker Mark , Robson Alex , Williams Jackson , Murphy Chris , O'Sullivan Liam 
Published: April 2019

Abstract
Insights from appraisal well tests can take months to incorporate into subsurface modelling, causing delays to development planning and resulting in key decisions being made using incomplete data and sub-optimal methods. This is due to the time-consuming process of updating or rebuilding reservoir models, simulating them and subsequently analysing the results. In this project, a combination of automated geomodelling, rapid dynamic simulation and statistical analysis were applied to reduce the time to insights from months to days. Well test pressure data was used to condition a suite of reservoir models and evaluate the impact on the optimal development scenario. The application of this process increased confidence in the decision and reduced the modelled probability of low-side outcomes. In addition, we trialled a process to deliver an improvement to the geological understanding of the field through a reduction in the model uncertainties. We also discuss an extension of this concept to perform a robust value-of-information assessment of appraisal or development planning decisions.