bluuebunny/crossref_metadata_2025_split huggingface dataset:
Overmature and Vitrinite-Barren Source Rocks: A Novel Thermal Maturity Parameter
Authors: Henderson Sebastian , Ghassal Bandar 
Published: March 2019

Abstract
Standard Rock-Eval pyrolysis is commonly used to estimate the thermal maturity of source rocks. However, measuring the maturity of overmature samples with high Tmax values (&gt; 470°C) is very challenging due to the weak development of S2 peaks. Moreover, measuring the vitrinite reflectance of dispersed organic matter high thermal maturity samples is commonly used when the Tmax (°C) of the sample is unreliable. Nevertheless, vitrinite assemblages are very rare/absent in marine samples particularly in marlstones or pre-Carboniferous source rocks. The current study addresses a new thermal maturity parameter that used the carbon monoxide CO released during Rock Eval-6 oxidations.
A total of 14 marine source rock samples were analyzed by Rock Eval-6 to assess their generative potential. The samples range in Tmax from 420° to 475°C indicating wide thermal maturity range from immature to overmature. During Rock-Eval analyses, CO released from the kerogens and their peak temperature (Tco) was recorded. A strong positive correlation was observed between the Tmax and the Tco (r=0.94). Note that the CO is released from the organic oxygen compounds that are none/or less liable compared to pure hydrocarbon compounds. Thus, Tco is more reliable than Tmax in assessing high thermal maturity levels.
The new method provides a robust and quick interpretation of high thermal maturity source rocks especially for pre-Carboniferous samples that lack a well-devolved S2 peak. Carbon monoxide generation is not affected by carbonate decay to CO2 and is also not affected by contamination used in drilling fluids. Testing of different source rocks is needed to establish this further and to improve the trend observed.
Beam Pump Dynamometer Card Classification Using Machine Learning
Authors: Sharaf Sayed Ali , Bangert Patrick , Fardan Mohamed , Alqassab Khalil , Abubakr Mohamed , Ahmed Mahmood 
Published: March 2019

Abstract
A beam or a sucker rod pump is an artificial-lift pumping system using a surface power source to drive a downhole pump assembly. A beam and crank assembly creates reciprocating motion in a sucker-rod string that connects to the downhole pump assembly. The pump contains a plunger and valve assembly to convert the reciprocating motion to vertical fluid movement. A dynamometer is a diagnostic device used on sucker rod pumped wells that measures the load on the top rod and plots this load in relation to the polished rod position as the pumping unit moves through each stroke cycle.
The analysis of the dynamometer card data delivers valuable insights on the status of the pump and indicates if future actions are required. In practice, the load versus displacement plot shape can be visually categorized in different classes where each shape has a specific meaning and indicates certain operating conditions. Machine learning algorithms are computing systems that learn to perform tasks by considering examples, generally without being programmed with any task-specific rules. During a period of approximately two (2) months, we collected 5,380,163 different cards from 297 beam pumps deployed in the Bahrain Field using the Supervisory Control and Data Acquisition (SCADA) system with an Open Platform Communication (OPC) interface. 35,292 cards are manually labelled by experts into twelve (12) classes. The dataset is split into 80% training and 20% holdout datasets. A training dataset is split into 5-fold cross validation. Different machine learning algorithms are evaluated predicting pump card class and their performance is compared. The top performing model, Gradient Boosting Machines (GBM) Classifier, achieves 99.98% accuracy in cross validation and 100% accuracy on holdout dataset without any extensive feature engineering.
This paper explains the steps taken to improve surveillance of beam pumps using dynamometer card data and machine learning techniques and the lessons learned from executing the first Artificial Intelligence (AI) project within Tatweer Petroleum.
A Novel Dynamic Production Splitting Method Based on the Catastrophe Theory
Authors: Mi Lidong , Hu Xiangyang , Jia Ying , Liu Qianjun 
Published: March 2019

Abstract
Multilayer commingled production is a common means for the development of multilayer tight gas reservoirs. The understanding of the production from each formation plays a significant role in the fine description of reservoir and remaining gas distribution, especially for the water-producing gas wells.
In this paper, a dynamic method for production splitting of water-producing gas wells is introduced. First, a dynamic permeability calculation method is established based on the relative permeability curve and the water-gas ratio. Then, based on the catastrophe theory, a dynamic production splitting method considering the influence of various factors (reservoir characteristics, development characteristics and geological characteristics) is established. Finally, a dynamic production splitting system is developed of multi-layer combined production gas wells.
The verification of production test data shows that the dynamic splitting method can better fit the production test results and accord with the actual production situation. This novel method can be used to split the production for the multilayer combined production well.
Application of Renewable Energy in the Oil and Gas Industry
Authors: Saadawi Hisham 
Published: March 2019

Abstract
The oil and gas industry will continue to play an essential role in meeting the growing world energy demand for decades to come. A fact sometimes overlooked is that the industry is also a major consumer of energy and a contributor to Greenhouse Gas (GHG) emissions.
In recent years, there has been increasing interest in the use of renewable energy in the oil industry. Various technologies are used to produce renewable energy, from solar radiation to harnessing the power of the wind and sea.
The application of different types of renewable energy in oil and gas fields is reviewed. The advantages and limitations of each technology are discussed. This paper presents the state of the art on current and potential applications of the different technologies of renewable energy in the oil and gas industry.
The paper demonstrates that using renewables offer many advantages including saving hydrocarbon resources, minimizing the oil industry's GHG emission and enhancing its public image.
Combined Low Salinity Water Injection and Foam Flooding in Sandstone Reservoir Rock: A New Hybrid EOR
Authors: Shabib-AsL Abdolmohsen , Abdalla Ayoub Mohammed , Abdalla Elraies Khaled 
Published: March 2019

Abstract
To date, several methods have been designed and implemented either individually or as combination to increase oil recovery. The combined methods also called hybrid Enhanced Oil Recovery (EOR) pose great challenges in the oil and gas industry. This is also confirmed from the Industry Technology Facilitator (ITF)'s Gulf Cooperation Council (GCC) Technology roadmap that pinpointed hybrid EOR as the top challenge. This work investigates the potential of a new hybrid EOR method termed Low Salinity Water Assisted Foam flooding (LSWAF). Typically, it consists of injecting Low Salinity Water (LSW) followed by an alternated injection of a Surfactant Aqueous Solution (SAS) and CO2 gas. The selection of LSW was based on its ability to produce significant changes of rock wettability. While the SAS was made, so that it results in a considerable reduction of IFT and produce foam that will remain considerably stable in the presence of oil under reservoir conditions. The selection of the best LSW and SAS followed a screening phase whereby formulations of different ion compositions and salinities were prepared and their performances were compared against each other. Tests were carried out by using two types of light crude oil with different Total Acid Number and Base Number, namely Crude oil A and B. In terms of changes in wettability, for both crude oils A and B, the experimental results revealed that much changes were experienced by the LSW of KCl composition followed by that of NaCl, then CaCl2, MgCl2, Mix composition, and finally the formation water (FW) that presented negligible changes. The changes with crude oil B were greater than crude oil A. However, for crude oil A and B, the core flooding experiments showed that the highest residual oil recovery was achieved by the LSWAF of KCl composition (87.45% of OOIP) compared to the MgCl2 and FW.
Hydrogen-Deuterium Exchange Between Rock Minerals and D2O
Authors: Zainuddin W N , Xie Sheena X. , Kechut Nor Idah , Kantaatmadja Budi P. , Singer Philip M. , Hirasaki George J. 
Published: March 2019

Abstract
Nuclear magnetic resonance (NMR) T2 spin-spin relaxation is a well-established technique in petrophysics labs for quantifying bound/free water and pore-size distribution of reservoir rocks. The method has also been used to measure oil and water saturations, and to characterize wettability alterations for oil/water/rock systems. The T2 relaxation distribution measured by hydrogen NMR is the sum of contributions from both oil and water in the core. It is therefore necessary to separate the T2 signals of oil from water. Since deuterium oxide (D2O) does not have a NMR signal at the resonance frequency for hydrogen, brine made with D2O is commonly used as the aqueous phase to determine the oil saturation from NMR.
The objective of this work was twofold: (1) to validate the oil saturations in the core with NMR T2 relaxation at connate water saturation (before and after aging) and residual oil saturation after waterflooding; and (2) to investigate the potential hydrogen-deuterium (H-D) ion exchange between rock minerals and D2O. Berea sandstone cores were used along with the crude oil from one of the fields in the Sarawak Basin, Malaysia. The aqueous phase was a synthetic brine made with either deionized water or D2O.
Two cores containing the crude oil with D2O brine as the connate (or initial) water were aged at 75eC for up to 65 days. During the aging period, the cores were scanned three times for T2 measurements. The measured T2 volumes (supposedly a measure of the oil volume) of the two cores kept increasing as the aging time increased. However, mass balance indicated that the oil saturation was the same before and after aging. The inconsistent oil saturation measured by NMR indicated that there was H-D ion exchange between the rock minerals and D2O. The cores were then flooded with the fresh D2O brine, after which the residual oil from NMR agreed with that from mass balance, indicating that the fresh D2O had replaced the connate D2O brine affected by H-D ion exchange.
Additionally, two cores fully saturated with D2O brine were also measured by NMR before and after aging at 75°C, again confirming the H-D ion exchange between the rock minerals and D2O. Finally, the mixture of the crude oil and D2O was measured by NMR before and after aging at 75°C, indicating that the interactions between the crude oil and D2O increased the T2 relaxation time. The total T2 volume was not affected.
This work provides evidence of H-D ion exchange between rock minerals and D2O at elevated temperature. It is recommended that such interactions between the rock minerals and D2O brine be considered for related tests, especially when elevated temperature is involved.
Diagnosing and Predicting Problems with Rod Pumps using Machine Learning
Authors: Bangert Patrick 
Published: March 2019

Abstract
Approximately 20% of all oilwells in the world use a beam pump to raise crude oil to the surface. The proper maintenance of these pumps is thus an important issue in oilfield operations. We wish to know, preferably before the failure, what is wrong with the pump. Maintenance issues on the downhole part of a beam pump can be reliably diagnosed from a plot of the displacement and load on the traveling valve; a diagram known as a dynamometer card. We demonstrate in this paper that this analysis can be fully automated using machine learning techniques that teach themselves to recognize various classes of damage in advance of the failure. We use a dataset of of 35292 sample cards drawn from 299 beam pumps in the Bahrain oilfield. We can detect 11 different damage classes from each other and from the normal class with an accuracy of 99.9%. This high accuracy makes it possible to automatically diagnose beam pumps in real-time and for the maintenance crew to focus on fixing pumps instead of monitoring them, which increases overall oil yield and decreases environmental impact.
Improved Predictions in Oil Operations Using Artificial Intelligent Techniques
Authors: Hassan Amjed , Al-Majed Abdulaziz , Mahmoud Mohamed , Elkatatny Salaheldin , Abdulraheem Abdulazeez 
Published: March 2019

Abstract
Oil is considered one of the main drivers that affects the world economy and a key factor in its continuous development. Several operations are used to ensure continues oil production, these operations include; exploration, drilling, production, and reservoir management. Numerous uncertainties and complexities are involved in those operations, which reduce the production performance and increase the operational cost.
Several attempts were reported to predict the performance of oil production systems using different approaches, including analytical and numerical methods. However, severe estimation errors and significant deviations were observed between the predicted results and actual field data. This could be due to the different assumptions used to simplify the problems. Therefore, searching for quick and rigorous models to evaluate the oil-production system and anticipate production problems is highly needed.
This paper presents a new application of artificial intelligent (AI) techniques to determine the efficiency of several operations including; drilling, production and reservoir performance. For each operation, the most common conditions were applied to develop and evaluate the model reliability. The developed models investigate the significance of different well and reservoir configurations on the system performance. Parameters such as, reservoir permeability, drainage size, wellbore completions, hydrocarbon production rate and choke performance were studied. The primary oil production and enhanced oil recovery (EOR) operations were considered as well as the stimulation processes. Actual data from several oil-fields were used to develop and validate the intelligent models.
The novelty of this paper is that the proposed models are reliable and outperform the current methods. This work introduces an effective approach for estimating the performance of oil production system and refine the current numerical or analytical models to improve the reservoir managements.
Experimental Study to Estimate CO2 Solubility in a High Pressure High Temperature HPHT Reservoir Carbonate Aquifer
Authors: Zaidin Mohd Fakrumie , Kantaatmadja Budi Priyatna , Chapoy Antonin , Ahmadi Pehzman , Burgass Rod 
Published: March 2019

Abstract
The X field is one of PETRONAS's Research & Development (R&D) initiative plans involving separation of high CO2-Hydrocarbon gas and CO2 storage in offshore Malaysia. The X field is a high pressure high temperature (HPHT) carbonate reservoir with a temperature of 423 K and pressure of 36.0 MPa with about 500 m of gas column. It was chosen as a candidate due to its well and reservoir data completeness. The plan is technically challenging as it involves re-injecting produced supercritical CO2 back into an aquifer reservoir for permanent storage. Recently acquired X field DST data indicates the presence of CO2 in the aquifer, up to a level nearing saturation. Information of the initial CO2 concentration level in the aquifer reservoir is critical to ensure the success of the CO2 injection. Predictions on this initial CO2 solubility have been made using available well data, however the reliability of the results has to be validated by an experimental study. Therefore, an extensive experimental approach to measure initial CO2 solubility in the X field aquifer reservoir has been conducted. As pressure, temperature and salinity are the important key parameters that influence CO2 solubility, detailed information about X field gas and aquifer brine compositions are well determined prior to the solubility measurement. Utilizing lab facilities at Heriot-Watt University (HWU), measurements were conducted at T=423.15 K and pressure at 36.0 MPa to mimic the X field aquifer conditions. The experimental results obtained are compared against available literature data, Duan Model and sCPA-EoS model and reasonable agreements were observed. Experimental results indicated that the X field aquifer is not fully saturated with CO2 and it could accommodate an additional 6 mol% of CO2 dissolved in the brine. In addition, approximately 6 mol% of hydrocarbon will be recovered from the same aquifer system as a result of CO2 injection due to the CO2-Hydrocarbon displacement. This paper details lab measurements of initial CO2 solubility in the X field aquifer, including preparation, experimental procedure, results and discussion as well as suggested future works. Reservoir simulation incorporating the experimental data obtained from this study is necessary and recommended, for getting a full picture of the CO2 injection program for the current Carbon Capture Utilization & Storage (CCUS) project.
Next Generation Gas Emission Monitoring System
Authors: Uyanik Ilyas , Wesley Avinash 
Published: March 2019

Abstract
During drilling or production operations, poisonous, highly flammable hazardous gases can be released into the environment. A next-generation gas emission monitoring system monitors gas leaks and can help the oil and gas industry improve workplace safety. The initial design, architecture, and development of a real-time monitoring and surveillance system consisting of drones capable of performing autonomous aerial inspections is discussed. This system monitors and reports the spatiotemporal evolution of hazardous gas clouds, such as H2S, CH4, and CO2, in the oil and gas facilities in real time and provides necessary actions for a safe operation. The proposed monitoring system is compared to the traditional monitoring approach where sensors are placed near the ground. This work is a significant improvement from the authors’ previous work leveraging state-of-the-art machine learning technologies to create smart drones capable of making intelligent decisions involving gas leak monitoring.
Validation of High Pressure Resin Impregnation Technique for High Resolution Confocal Imaging of Geological Samples
Authors: Hassan A. , Yutkin M. P. , Chandra V. , Patzek T. W. 
Published: March 2019

Abstract
In this paper, we present a procedure for high pressure resin impregnation of microporous rock. This procedure produces the high- quality pore casts that reveal the fine details of the complex pore space of micritic carbonates. We carefully test our resin impregnation procedure and demonstrate that it renders the high resolution, 3D confocal images of pore casts. In our work, we use silicon micromodels as a reference to validate the key parameters of high-pressure resin impregnation. We demonstrate possible artifacts and defects that might develop during rock impregnation with resin, e.g., the resin shrinkage and gas trapping. The main outcome of this paper is a robust protocol for obtaining the high-quality epoxy pore casts suitable for rock imaging with Confocal Laser Scanning Microscopy (CLSM). We have implemented this protocol and provided the high resolution, three-dimensional (3D) imagery and description of microporosity in micritic carbonates.
Complex Settings Need Complex Equations – Comparison Between Numerical and Semi-Quantitative Geomechanical Solutions
Authors: Bahuguna Somessh , Talreja Rahul 
Published: March 2019

Abstract
Simplified semi-quantitative equations are used in 1D geomechanics workflows to predict the rock’s behavior during drilling and production. While such methods allow for getting a time- efficient solution, it does lose out on accuracy. In addition, by simplifying equations, we limit our ability to predict behavior of the borehole wall only i.e. near wellbore solutions. We lose the ability to predict full field behavior in response to drilling and production activities. For example, when constructing a field-wide drilling plan or a field development plan for a complex subsurface setting, a simplified approach may not be accurate enough and on the contrary, can be quite misleading. A 3D numerical solution on the other hand, honours subsurface features of a field and simulates for their effect on the stresses. It generates solutions which are more akin to reality.
In this paper, this difference between a simplified semi-quantitative well-centric approach (1D) and a full field numerical solution (3D) has been presented and discussed. The subsurface setting considered in the study is quite complex – an amalgamation of high dipping beds with pinch outs and low angled faults in a thrust regime. Wellbore stability and fault stability models have been constructed using both a well-centric approach and a full field-wide 3D numerical solution.
It is clearly observed that field-based approach provided us more accurate estimation of overburden stresses, variation of pore pressure across the field, impending changes in stress magnitudes and its rotation due to pinch-outs and formation dips. For example, due to variation in topography, the estimated well-centric overburden at the toe of deviated well at reservoir level is lower by 0.21gm/cc (~1.75ppg~0.9psi/ft) as compared to the 3D model. It is also observed that within the field itself stress regime changes from normal to strike slip laterally across the reservoir. In comparison to 1D model, considerable differences in stable mud weight window of upto 1.5ppg is observed in wells located close to faults. This is primarily due to effect of fault on stresses (both magnitude and azimuth). Stress states of 4 faults were assessed and all 4 faults are estimated to be critically stressed with elevated risk of damaging three wells cutting through. However, a simple 1D assessment of stress state of faults at wells cutting through them, shows them to be stable.
By comparing the differences between 1D and 3D solutions, importance of 3D numerical modelling over 1D models is highlighted.