bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Effect of High-Geothermal Field on the Diagenesis and Pore Evolution of Sandstone Reservoirs in Baiyun Sag, Pearl River Mouth Basin
Authors: Ma YongKun 
Published: April 2019

Abstract
Oil and gas exploration in the deep-water areas have become a global hot spot. The deep-water area of the Baiyun sag in the Pearl River Mouth Basin is an important exploration target. The area is a typical deep-water hot basin of a wide range of geothermal gradients. Data from a single borehole shows a geothermal gradient from 4.0 to 6.64°C/100m. High geothermal field has an important control on the reservoir diagenesis, pore evolution and porosity-permeability trends. We analyzed sandstone samples from the ZhuJiang and ZhuHai Group, which were buried in the depth range between 500- and 4000m, and display similar composition and textures. The samples can provide insights into the evolution of reservoir diagenetic features under progressive burial process. We also analyzed sandstone samples frome EnPing Group. In general, the petrological composition was the main controlling factor of reservoir quality. The high geothermal field led to a rapid decrease in the porosity and permeability of deeply buried sandstones. Howerver, the EnPing Group, which has a deeper burial depth, shows good reservoir quality. Compared with the ZhuJiang Group and the ZhuHai Group sandstone, the EnPing Group sandstone is dominantly coarse sandstone with more quartz grains, minor feldspars and rock fragments. The EnPing Group is dominated by primary pores, which has a better porosity-permeability relationship than other groups. The deep-water of the Baiyun sag still has potential for exploration. In particular, EnPing Group sandstone reservoir may become a desirable goal in deep and ultra-deep exploration.
Integrating Qualitative and Quantitative Drilling Risk Prediction Methods for Shale Gas Field in Sichuan Basin
Authors: Wang GaoCheng , Zhao Chunduan , Liang Xing , Pan Yuanwei , Li Lin , Wang Lizhi , Rui Yun , Li Qingshan 
Published: April 2019

Abstract
Huangjinba shale gas field is located at the south edge of the Sichuan Basin. It has very complex structures, in situ stresses and natural fracture corridors in comparison to adjacent areas in the Sichuan Basin. In recent drilling campaigns, drilling risks have caused some wells to fail in reaching their planned total depth, eventually failing to deliver cost-effective gas production. In order to mitigate drilling risks, e.g. mud loss, collapse, stuck, hang up, gas kick, effective drilling risk prediction is an urgent challenge to address. Integrating quantitative drilling risk prediction methods with qualitative methods could increase the prediction accuracy and avoid or mitigate the drilling risk during the well deployment stage.
In this project, multiple seismic attributes were used to predict natural fracture distributions which qualitatively indicated the locations where drilling risks were likely occur. Comprehensive geophysical characterization was performed to identify natural fracture zones and patterns, and their mechanisms were validated by analyzing regional geological and tectonic evolution.
Image log data was then integrated into the natural fracture distribution prediction from seismic to build a DFN (Discrete Fracture Network). This combination of the DFN predicted from seismic data plus quantitative image log information allowed improved accuracy in the prediction of drilling risks.
Following this, natural fracture stability was analyzed by building a 3D geomechanics model in order to predict drilling complex qualitatively. A full field 3D geomechanics model was built through integrating seismic, geological structure, log and core data. The 3D geomechanical model includes 3D anisotropic mechanical properties, 3D pore pressure, and the 3D in-situ stress field. Through leveraging measurements from an advanced sonic tool and core data, the anisotropy of the formation was captured at wellbores and propagated to 3D space guided by prestack seismic inversion data. 3D pore pressure prediction was conducted using seismic data and calibrated against pressure measurements, mud logging data, and flowback data. The discrete fracture network model, which represented multi-scale natural fracture systems, was integrated into the 3D geomechanical model during stress modeling to reflect the disturbance on the in-situ stress field by the presence of the natural fracture systems.
From these models, a drilling map which quantitatively indicated the depth where drilling risk such as mud loss, gas kick, etc. occurred was created along the well trajectory.
This paper presents the highlights and innovations in seismic multi-attributes analysis and full-field geomechanics modeling which integrate qualitative and quantitative methods for drilling risk prediction.
A New Method to Detect Influx and Loss During Drilling Based on Machine Learning
Authors: Shi Xiaoyan , Zhou Yingcao , Zhao Qing , Jiang Hongwei , Zhao Liping , Liu Yong , Yang Guang 
Published: April 2019

Abstract
Influx and loss are the two most common downhole complexities. They not only cause the reservoir damage, increase the exploration cost, reduce the drilling efficiency; but also induce major malignancy. Therefore accurate and early detection of influx and loss during drilling is of great significance. Traditional influx and loss detection methods have the shortcoming of monitoring time lagging and high costs. As the rapid development of artificial intelligence techniques, researchers start to detect influx and loss using artificial intelligence method. This work adopted two machine learning algorithms(Random forests and Support vector machine) according to their characteristics to detect influx and loss during drilling in real-time. The detection methods includes four steps: 1) Generating raw influx/loss raw data set by combining real-time drilling data and drilling history data; 2) Pre-processing raw data set to obtain training data set; 3) Training classification model of random forests and SVM by training data set and algorithms; 4) Predicting influx/loss by the trained model according to the new real-time data. The case study shows that influx and loss can be detected accurately in early stage by both random forests method and SVM method after proper pre-processing the raw data and optimizing algorithm parameters. The detection accuracy of the sample data from four wells exceeds 90%. This work demonstrate a new way to detect influx and loss by utilizing huge drilling data and machine-learning algorithms, and the detection results are satisfying.
Study of Seismic Data Processing Techniques in Deepwater Slope
Authors: Li Qingshou 
Published: April 2019

Abstract
Seismic data processing in deep-water slope belt area has always been a challenge for marine seismic data processing. Steep slopes could induce strong diffraction multiples, difficult velocity modelling, weaker illumination. This paper will introduce three key methods on dual direction acquisition seismic data imaging for slope belt area.
The first key method is joint multiple attenuation, this method uses 3D SRME,vertex drift high-precision radon multiple attenuation, super CDP gather median filter to multiple attenuation; The second key method is geological constrained velocity modeling. This method uses geological information to restrict the initial velocity which could reduce the impact of rapid land surface change on velocity modeling; The third method is dual direction data merge. After PSDM, this method uses fast and slow wave velocity correction and advantage data to get a dual direction merging data stack section.
Joint multiple attenuation could eliminate diffraction multiples effectively, and it can improve the signal to noise ratio which will be benefit for the velocity analysis and final imaging; Constrained velocity modeling could improve the velocity model accuracy in the steep slope area, which can help final imaging to avoid imaging distortion under steep seabed; To get a dual direction merging data stack section is a relabel way to increase data folds and illumination and improve the quality of imaging faults.
The successful application of these three methods shows that dual direction acquisition data processing provides an effective way to solve the imaging difficulties in steep slopes in deep water.
Unlocking and Materializing Development Opportunities with an Optimized Subsurface Study Approach and Adaptive Execution in a Brownfield
Authors: Tan Yeen Voon , Amiruddin Nurul Azrin , Li Hui Ming , Sim Chee Hui , Abdul Aziz Shahrizal , Adnan Nursyazwani , Mansor Mohd Najmi , Anuar Mohd Azlan , Jacobs Stephen Thomas , Khalid Aizuddin , Kumar Ashok 
Published: April 2019

Abstract
This paper discusses an alternative study approach with multiple lessons learned from a recent successful infill drilling campaign in a medium-sized brownfield. The team conducted a practical and an optimized subsurface study, including the no-frills classical reservoir engineering and a simple sector model to justify the infill wells. From idea generation to monetization of the barrels, it was fast and cost effective without compromising technical assurance.
Results of the infill drilling campaign and lessons learned are discussed in this paper. This is a case study on how a comprehensive understanding of reservoir complexity using creative data integration can be an adequate tool for field development. The results of the campaign demonstrated that an optimized study helps the operator and partners make an efficient investment decision in materializing development opportunities.
Refining History Matching Quality through Various Data Acquisition Campaign for a Major Waterflood Reservoir to Unleash Its Maximum Recovery Potential: A Case Study
Authors: Jamaludin Izzuddin , Mandal Dipak , Arsanti Dian , Dzulkifli Izyan Nadirah , Zakaria Nurul Azami , Mohamad Salleh Salhizan , Ahmad Hawari Saiful Adli , Mohd Azkah Mohd Zubair 
Published: April 2019

Abstract
Data acquisition remains one of the crucial activities to be consistently executed throughout field life for any oilfield development. Significant operating expenditure (OPEX) is allocated each year to understand reservoir performance, thus reduce uncertainties and enable optimizations. This paper aims to highlight the issues faced during simulation model history matching (HM) process of a waterflood reservoir, including understanding of depositional environment and production data integrity. The output is utilized to improve recovery factor (RF) via infill opportunities and water injection optimization.
Field A has run a second shot of 3D seismic in 2006 (first in 1995) and processed into a time lapse, 4D seismic. In 2014, a cased hole logging campaign utilizing the high precision temperature, spectral noise logging (HPT-SNL) tool has been completed to check the integrity and flow contribution of 12 wells in Reservoir-X. Within the same period, a pulse pressure testing (PPT) was carried out to verify the communication between wells, in addition to acquiring regular surveillance data which helped to improve reservoir simulation study.
The 4D seismic helped to understand the areal waterflood front movement and explained the water cut trend anomaly in an updip well which experienced earlier water breakthrough than near downdip producers. Moreover, it helped to identify a bypass oil zone which can potentially be an infill location. As most of the wells are on dual string completion, the HPT-SNL campaign helped to improve production allocation of multi stacked reservoirs as well as identify problematic wells which required rectification jobs. The PPT assisted in identifying a baffle zone to explain the poor pressure support observed in some producers in the south from the nearby water injectors. All data interpretations were incorporated into final HM model which subsequently identified infill locations and the reservoir management plan (RMP) was successfully revised. An infill program was executed in 2015, which successfully secured additional EUR of ~9 MMstb. Based on the studies and outcome of the infill campaign water injection optimization helped to improve production and added ~2 MMstb reserves, through voidage replacement ratio (VRR) optimization and oil producer (OP) to water injector (WI) conversion. With these efforts, team could successfully project RF of &gt;55%.
This case study demonstrates how acquiring focused surveillance data and their effective integration in performance analysis in simulation study helps to reduce uncertainties, unveils infill opportunities, improves production injection optimization and thus helps to improve the recovery factor in brown fields.
Seismic Facies Recognition and Stratigraphic Trap Characterization Based on Neural Networks
Authors: Zhang Si-Hai , Xu Yin , Abu-Ali Mahdi , Teng Mee Kee 
Published: April 2019

Abstract
Reservoirs and the lateral seal of stratigraphic traps are controlled by the depositional environment or diagenesis. The recognition of facies and lithology from seismic attributes is an effective approach for identifying stratigraphic traps related to the depositional environment. In this paper, the occurrence of stratigraphic traps related to depositional environment in Permian aeolian clastics and Jurassic carbonate-evaporites was studied. To identify these stratigraphic traps, multiple seismic attributes were classified using supervised and unsupervised artificial neural networks (ANNs), which allowed the recognition of seismic facies and lithology.
Neural networks are a powerful classification technique, which incorporates multiple attributes into a number of classes to identify sedimentary facies. Two algorithms comprising supervised and unsupervised neural networks are commonly implemented. With a supervised learning algorithm, prior information such as typical facies at the control wells are required to train the multilayer perceptron (MLP) network. With an unsupervised algorithm, only seismic data is input to the neural network, and competitive-learning techniques are employed to classify or self-organize the data based on its internal characteristics. Without prior information, the output classes are not labeled with lithofacies. According to the availability of prior information, supervised and unsupervised learning were applied to recognize dune-playa and carbonate-evaporite combinations, respectively. To characterize the depositional environments, joint interpretation with a geological model is necessary for both supervised and unsupervised classification.
Two major findings have been derived from this work. First, the learning technology based on ANNs is effective to recognize sedimentary facies. The microfacies and lithologies identified by both supervised and unsupervised ANNs are very consistent with the drilled wells. Second, the recognition of depositional facies and lithology can characterize the stratigraphic traps in the study areas. Lateral seal plays a key role in stratigraphic traps. Playa siltstone and tight lagoonal limestone constitute the lateral seal in dune-playa and carbonate-evaporite combinations, respectively.
Total Well Management : Maximising Well Lifecycle Value – A Regulator Perspective
Authors: Guillory Ryan , Wan Razali Wan A. Aziz , Masoudi Rahim , Singh Ratan , Ahmad Nur Asyikin 
Published: April 2019

Abstract
The 2015 oil price downturn and future uncertainty in the industry have resulted in Malaysia Oil and Gas Regulator PETRONAS (MPM) taking a more proactive role in driving Malaysian Petroleum Arrangement Contractors (PACs) to maximize lifecycle value from their well stock. Key focus areas include streamlining review processes, reducing well cost, maximizing production from active wells and reactivating idle wells, reducing failures from well activities, driving value driven surveillance and promoting low cost well abandonment. This paper will focus on how the objective & value driven approach by PETRONAS MPM have resulted in business performance improvements at the national level through proactive and effective well value maximization throughout field/well lifecycle, from exploration stage down to the decommissioning and abandonment stage.
A Model Designed for HSE Big Data Analysis in Petroleum Industry
Authors: Wu Tongxin , Mao Yaming , Zhao Gang 
Published: April 2019

Abstract
HSE (Health, Safety, and Environment) management is one of the most concerned matters of every business, especially in petroleum Industry. Currently, analyzing the origin of accident and tracing the responsibility of accident commonly happened after the accident due to the lack of analytical theories, methods and models. This paper presents a HSE big data analysis framework which is capable of analyzing historical data of HSE management to promote the practicality and scientificity of HSE management.
This paper has done much research of HSE data analytics. Based on the features of HSE management in petroleum Industry, it elaborate the open source projects and its applicable scenes in data analytics. Then, it gives suggestions of choosing open source projects to establish data analytics platform under given conditions. Last, by using data warehouse, data mining, machine learning and pattern identification technology, a HSE big data analytics framework was presented in this paper. This framework includes the level of data acquisition, data storage, data processing, data analysis, and data application.
The efficient use of this model can help to untangle doubts of HSE big data analytics, discover the regularity and characteristics of accidents, and enhance supervision and warning of safety production.
From Molecules to Barrels: A Case Study on Redefining Hydrocarbon Spectrum and DNA Tracing Through Gas-Chromatograph Fingerprinting
Authors: Zulkipli Siti Najmi Farhan , Harun Norhana 
Published: April 2019

Abstract
Geochemistry plays a key role in oil and gas business and often, it has the reputation of providing the most economical way to establish the ground truth for any analytical work done to trace hydrocarbon presence. Conventional ways in determining hydrocarbon fluid type and flow potential such as wireline formation tester, optical fluid analyzer, well testing, downhole and surface fluid samples could be an advantage or a headache if delineation of hydrocarbon presence is masked by high contamination from drilling fluid or non-representative samples. Often whenever any sudden major production hiccups occur, many factors come in which may cloud the real root cause identification. Hence, geochemistry method offers a unique solution in tracing the hydrocarbon presence and also the possible sources where it originates from. Methodology and principles of gas-chromatograph (GC) fingerprinting, case studies for application and value creation to the business are the scopes of this paper.
Examining the DNA and composition unique to each hydrocarbon fluid sample in the lab can be an intriguing process which requires shorter time compared to conventional analytical work. Requiring only few drops of hydrocarbon fluid, synthetic-based mud and base oil samples as input into the GC spectrometer machine, the unique chromatogram signature from each fluid will be overlaid onto each other for comparison and quantification of contamination level.
The case studies presented in this paper will highlight the key characteristics of live hydrocarbon signature as compared to the dead oil or drilling fluid signature which acts as the outlier or contaminant to the samples. Values created in terms of proving the hydrocarbon discovery, refining well testing decision based on the fingerprinting results which involves stakeholder's interest, determination of potential well barrier leaks, optimizing well stimulation design and possible sources of hydrocarbon migration into the wellbore will also be highlighted.
In a nutshell, application of GC fingerprinting to ascertain hydrocarbon fluid type is successfully proven, cost effective and technically viable approach. Recognizing the DNA and unique signature of each fluid will be an added advantage for short term and long term business investment strategies.
Prediction of Pore and Fracture Pressures Using Support Vector Machine
Authors: Ahmed S. Abdulmalek , Mahmoud Ahmed Abdulhamid , Elkatatny Salaheldin , Mahmoud Mohamed , Abdulraheem Abdulazeez 
Published: April 2019

AbstractPore and fracture pressures are a critical formation condition that affects efficiency and economy of drilling operations. The knowledge of the pore and fracture pressures is significant to control the well. It will assist in avoiding problems associated with drilling operation and decreasing the cost of drilling operation. It is essential to predict pore and fracture pressures accurately prior to drilling process to prevent various issues for example fluid loss, kicks, fracture the formation, differential pipe sticking, heaving shale and blowouts.Many models are used to estimate the pore and fracture pressures either from log information, drilling parameters or formation strengths. However, these models have some limitations such as some of the models can only be used in clean shales, applicable only for the pressure generated by under-compaction mechanism and some of them are not applicable in unloading formations. Few papers used artificial intelligence (AI) to estimate the pore and fracture pressures. In this work, a real filed data that contain the log data and real time surface drilling parameters were utilized by support vector machine (SVM) to predict the pore and fracture pressures.Support vector machine predicted the pore and fracture pressures with a high accuracy where the coefficient of determination (R2) is greater than 0.995. In addition, it can estimate the pore pressure without the need for pressure trends and predict the fracture pressure from only the real time surface drilling parameters which are easily available.