bluuebunny/crossref_metadata_2025_split huggingface dataset:
Productivity Assessment and Evaluation of Water Coning Tendency to Support Full Field Development Plan Using Single Well Numerical Model
Authors: Algdamsi Hossein Ali , Agnia Ammar , Amtereg Ahemd 
Published: October 2017

Abstract
In the presence of an active aquifer, water coning in a vertical wells phenomenon may cause a significant Reduction of the well productivity, thus reducing the ultimate oil recovery, increasing the cost of production operations, and arising environmental problems related to water disposal. After water breakthrough at a well, the water phase may become the dominant one, and the well must be shut in because oil production becomes uneconomically. A wide number of analytical approaches derived for homogeneous formations were developed to analyse coning phenomena and to define a valid methodology to evaluate the critical rate to consider the impact of formation heterogeneity and flow barrier distribution the combination of geological and flow simulation software. The critical rate is the maximum oil rate that can be produced before water breakthrough occurs. However, due to the complexity of the problem, a rigorous approach of general validity could not be identified. The present study was aimed at providing an exhaustive comparison among the most used analytical methods and the results obtained by numerical simulation for water coning and prediction for a number of possible scenarios to quantify the water coning potential recovery dynamics prior to and after water breakthrough, and water cut control with production rate in the field assessing and well productivity investigates dynamics of productivity loss in the presence of uncertainty of real field geological properties by optimizing new wells targeting and well operation parameter, which provided an increase of water-free periods of the wells. Two set of sensitivities were conducted in this evaluation the first one refers to the reservoir characterization (variation of relative permeability curve, wettability, transition zone, capillary effect, vertical to horizontal permeability ratio distribution) and the second to the completion strategy and well operation parameter (perforation interval, standoff, and geological zone). the single well model showed that the favourable mobility ratio, permeability profile and fluid characteristics has huge impact on efficient displacement and to minimizes the water coning tendency in the field the recommended stand-off and drawdown for future well therefore the high off take rates could be achieved with no impact on ultimate recovery considering the maximum efficient rate MER allowed is directly related to the productivity index of the well.
Selection Criteria for Miscible-Gases to Enhance Oil Recovery in Unconventional Reservoirs of North America
Authors: Alfarge Dheiaa , Wei Mingzhen , Bai Baojun , Alsaba Mortadha 
Published: October 2017

AbstractUnconventional resources have played a significant role in changing oil industry plans recently. Shale formations in North America such as Bakken, Niobrara, and Eagle Ford have huge oil in place, 100-900 Billion barrels of oil in Bakken only. However, the predicted primary recovery is still low as 5-10%. Therefore, seeking for techniques to enhance oil recovery in these complex plays is inevitable. In this paper, two different approaches have been integrated to investigate the feasibility of three different miscible-gases which are CO2, lean gases, and rich gases. Firstly, numerical simulation methods of compositional models have been incorporated with Local Grid Refinement (LGR) of hydraulic fractures to mimic the performance of these miscible gases in shale-reservoirs conditions. Furthermore, implementation of a diffusion model in the LS-LR-DK (logarithmically spaced, locally refined, and dual permeability) model has been conducted. Secondly, different molar-diffusivity rates for miscible gases have been simulated to find the diffusivity level in the field scale by matching the performance of some EOR pilot-tests which were conducted in Bakken formation of North Dakota, Montana, and South Saskatchewan.This study approved that diffusion flow is dominated in these types of reservoirs. Therefore, the injected CO2 needs a significant molar-diffusivity into formation-oil, so it can penetrate into shale-matrix and enhance oil production. However, some of CO2 Pilot-tests showed a good match with the simulated cases which have low molar-diffusivity between the injected CO2 and the formation-oil. Accordingly, the rich and lean gases have shown a better performance to enhance oil Recovery in these tight formations. However, rich gases need long soaking periods and lean gases need large volumes to be injected for more successful results. Furthermore, number of huff-n-puff cycles has a little effect on the injected-gases performance; however, the soaking period has a significant effect. This research project demonstrated how to select the best type of miscible gases to be injected in unconventional reservoirs according to the field candidate conditions and operating parameters.
Next-Generation Gelling Agents for Improved Displacement in Enhanced Oil Recovery Processes
Authors: Temizel Cenk , Zhang Ming , Biopharm Frontida , Jia Bao , Putra Dike , Moreno Raul , Al-Otaibi Basel , Alkouh Ahmad 
Published: October 2017

Abstract
Per recent analyses, in the near future, over half amount of the oil extracted globally will require some form of enhanced oil recovery (EOR) technique. Existing literature and historical investigations suggest that in oil reservoirs having viscosities between 10 — 150 m. Pa.s, there is a substantial prospective for tertiary recovery through the implementation of polymer flooding. For reservoir oil viscosities above 150 mPa.s, the polymer pumping efficiency goes down as polymer injectivity reduces significantly with increasing injection water viscosity that is used to attain a favorable mobility ratio at such high oil viscosities. To overcome this limiting factor, in this study, we propose the use of supramolecular assemblies (SMA) that have adjustable viscosity properties. Complex long-chain amino-amides and maleic acid are used to make these assemblies, which allow it to have reversible viscosity depending on the solution pH level.
To maintain high injection efficiency, during pumping, SMA solutions will be kept at low viscosity values. On entry in deep reservoir or at oil contact phase, through introduction of an external stimulus, the viscosity of SMA solution will be reversed to a much higher viscosity. This will allow to sufficiently improve the mobility ratio. Preliminary results from lab-scale studies have indicated that along with reversibly adjustable viscosity property, SMA solutions are also tolerant to high temperatures and salt concentrations.
Supramolecular solutions can be contemplated as remedy polymer systems, since unlike conventional polymers they disassemble and re-assemble when exposed to high temperature and stress conditions. In such conditions, conventional polymers generally undergo degradation. Additionally, through molecular scission processes SMA solutions can also be used in highly confining environments as well as in permafrost conditions and thin zones where conventional thermal techniques are not applicable.
The objective of this work is the development of a novel SMA system that has the aforementioned properties of reversibly adjustable viscosity through pH, tolerance to high temperature and salt concentrations through desired interfacial properties. Lab-scale preliminary results have shown the potential economic benefits of the use of SMA solutions on a field-wide scale. Based on the results, it must be emphasized that SMA systems have a worldwide application in oil reservoirs for EOR purposes.
Potential for Solar Energy Use in the Global Petroleum Sector
Authors: Wang J.. , Brandt A.. , O'Donnell J.. 
Published: October 2017

Abstract
The global oil industry is a major user of energy in extracting, transporting, and refining hydrocarbons.
We have previously reported on the potential for the practical use of solar energy in these operations, including both extraction and transport ("upstream") and refining ("downstream"), considering both the use of solar electricity from photovoltaic (PV) generation and the use of solar thermal energy in the form of steam from concentrating solar collectors. This work is available in the journal Energy (118:884-892) (https://doi.org/10.1016/j.energy.2016.10.107).
In this report we provide some further background on the solar technologies we considered for oil industry use, and update our previous assessments. The results show that potential solar thermal capacity ranges from 34 to 88 GWt in upstream operations, and from 42 to190 GWt in current downstream operations. Potential PV deployment ranges from 7 to 12 GWe in upstream operations and 17 to 95 GWe in current downstream operations. New developments are planned and underway in Kuwait; these new upstream and downstream projects collectively could use solar thermal capacity of 68 GWt and 3.2 GWe PV, and constitute a major fraction of the worldwide potential.
Effect of Alkaline Agents on the Transport of Polysaccharides in a Carbonate Matrix
Authors: Servin Jesus M. , Ellis Erika S. , Shateeb Hussain , Schmidt Howard K. 
Published: October 2017

Abstract
Nanoparticles have potential for multiple applications in the oil and gas industry, ranging from EOR operations to improved drilling fluids and cements, as well as reservoir monitoring and characterization. The two biggest challenges to overcome for nanoparticles in the reservoir are stability at reservoir conditions and retention. Understanding the mechanisms controlling transport and retention in porous media is essential to develop nanoparticles able to successfully traverse the reservoir. A commonly used strategy to stabilize nanoparticles and reduce retention is to functionalize their surface or to coat them with polymers. Although the coatings help stabilize the nanoparticles, retention is still a major challenge preventing field wide applications. Previous studies have shown that increasing the pH of injected water reduces the amount of surfactants (chemically similar to polysaccharides) adsorbed to the rock. This paper presents the results of a study that investigates the possibility of improving the transport properties of a polysaccharide coating (dextran) through a carbonate matrix by altering the pH using an alkaline passivation agent (sodium metaborate). The amount of retention was quantified by fluorescently tagging the dextran and measuring the intensity of fluorescence of the effluents using a portable spectrometer. The intensity of fluorescence of the effluents should be proportional to the amount of dextran being recovered. The results show that the alkali passivation agent reduces retention of the coating by ~16%.
Standardizing Reservoir Management Best Practices in KOC
Authors: Capello Maria A. , Al-Bahar Mohammad , Sizer Jim , Kansao Rami , Al-Mutairi Fahad , Al-Shammari Hanaa S. 
Published: October 2017

AbstractKuwait's oil and gas reservoirs shape one of the largest reservoir portfolios worldwide, meriting savvy and forward-looking reservoir management practices and workflows to maintain the country as a reliable supplier for many decades to come. Reservoir management can be defined as a body of principles, processes, and practices that collectively governs the way a field is managed. It is focused on the subsurface, but not exclusively. Reservoir management is a stewardship process that significantly impacts production performance, bookable reserves, estimated ultimate recovery (EUR), and capital expenditures (CapEx). Kuwait Oil Company (KOC) has set out to optimize and standardize its reservoir management practices at a corporate level (Capello et al. 2016).This journey is still in progress, and has several strategic objectives: a) standardize reservoir management practices across KOC assets; b) ensure the optimization of recovery factors and life of fields strategies for each reservoir enabling the simultaneous production of nearby, connected or stacked producing intervals, with different maturity levels; c) extend production plateaus in time for each KOC asset; and d) ensure the active participation in reservoir management activities of a skilled workforce.The ultimate aim is to apply industry best practices in managing the oil and gas fields in Kuwait. The initiative is called Reservoir Management Best Practices (RMBP), and comprises a two-fold effort:A Steering Committee composed of 10 managers is charged with the implementation of reservoir management best practices policies and guidelines across all KOC assets. It was essential that management endorsed the initiatives and assured buy-in from other managers, team leaders and seniors in all assets and support organizations within KOC. Six Process Implementation Projects (PIP) were tackled with key objectives and elements of RM. It is envisioned that these PIPs will impact and uplift the way all assets work, so technical staff participation and buy-in was paramount for a successful implementation.The approach taken by KOC merges technical emphasis and international best-in-class practices with change management and communication strategies to align efforts. Most importantly, this solidifies and builds commitment and a shared motivation to achieve common goals.This paper summarizes the main accomplishments of KOC's RMBP to date, which span from workflows and governance in data management, to automation of drilling proposals and analog databases for reservoirs. KOC estimates these new processes will reduce operating costs and increase capital efficiency, which in turn will increase profitability by up to 15%.
New Methodology to Evaluate the Performance of Chemical Sealant Loss Circulation Materials
Authors: Abdulrazzaq Waseem , AlBuraikan Reem , Savari Sharath , Whitfill Donald L. 
Published: October 2017

Abstract
A new laboratory work procedure has been developed to evaluate and test the performance and effectiveness of chemical-sealant-based loss circulation materials (CS-LCMs), which are often used in cases of severe-to-total losses. These unconventional testing methods should be useful tools to evaluate the integrity of loss circulation material (LCM) products under downhole conditions in terms of differential pressure buildup and how quickly such LCMs can arrest lost circulation.
Evaluation and testing of LCMs in the laboratory before field application are crucial. Conventionally, the plugging capacity of particulate LCMs is tested against various-sized slotted discs using a permeability plugging apparatus (PPA), and integrity is tested in terms of sealing capacity and fluid loss value. Testing the performance of CS-LCMs required another means that included plugging extra-large vugs and building a significant differential pressure that could sustain the drilling fluid column. Pumpability of CS-LCMs and mechanical strength performance over time were evaluated using a high-pressure/high-temperature (HP/HT) consistometer, ultrasonic cement analyzer (UCA), and modified PPA following this fit-for-purpose procedure.
Extensive laboratory testing revealed that the new testing method was highly compatible with almost all types of chemical-based LCMs, including resin, gunk squeeze, and thixotropic slurries. The effectiveness and performance of several commercially available CS-LCMs were measured using different vug sizes (i.e., up to tens of millimeters). Thickening time of LCMs were observed pumpable [i.e., &lt;70 Bearden units of consistency (Bc)], even after hours of conditioning at bottomhole circulating temperatures (BHCTs). As per API routine practice, tested slurry is deemed unpumpable if Bc exceeds 70. However, the thickening time of gunk squeeze LCMs were observed to be significantly high in a short interval of time once aqueous and nonaqueous streams mixed together. Gunk-based LCMs build high differential pressures and compressive strength over the same periods of curing time at bottomhole static temperature (BHST) and pressure compared to thixotropic-based LCMs.
Appropriate laboratory testing and evaluation of chemical-based LCMs under downhole conditions are highly recommended before field trail/application. This new testing/evaluation method should help minimize operational risk and nonproductive time (NPT) at the rig site.
Application of Real-Time Data and Integrated Models in an Automated Workflow for Production Surveillance and Analysis in Burgan Oil Field
Authors: Al-Shammari B.. , Rane N. M. , Desai S F , Sabea Al , Hamad Salem , Pandey M.. , Shankhdhar S.. , Chacko R.. , Ledesma S. F.. 
Published: October 2017

Abstract
The GC-01 KwIDF system has been deployed in the East Kuwait area of Burgan oil field and includes real time production surveillance instrumentation for wells and production facility. The raw data from the wells and facility is integrated with well and network models in an automated workflow process for surveillance and analysis of the GC-01 production.
The Burgan oil field integrated digital field (KwIDF) automation system utilizes wellhead pressure and water-cut measurements for automated calibration of well and surface network models. The KwIDF system workflow utilizes calibrated well models and real time data to identify changes in GC-01 daily production and reconciles it with the contributing wells. The workflow identifies wells with production changes and associates them with key well and facility parameters for instant diagnosis and analysis of daily production issues.
This paper describes the application of real time data and integrated models in an automated workflow for quick identification and analysis of daily field production.
Examples describe how the workflow has been used in quick analysis and decision making to optimize daily production and achieve sustainable targets in one of the production facilities of the Greater Burgan Field in Kuwait.
Application of Artificial Intelligence Techniques in Estimating Oil Recovery Factor for Water Derive Sandy Reservoirs
Authors: Ahmed Ahmed Abdulhamid , Elkatatny Salaheldin , Abdulraheem Abdulazeez , Mahmoud Mohamed 
Published: October 2017

AbstractOil and gas operating companies are always concerned with evaluating the reserve of their assets. Evaluation process of hydrocarbon reserves requires a full understanding and knowledge of technical and non-technical aspects regarding the nature of reservoir, available technology and economic conditions as well as others. Recovery factor (RF) is the most important parameter in evaluating the reserve of new fields.Several techniques are currently available for estimating oil recovery factor, the accuracy of those techniques are highly affected by data availability which is mainly related to the field age. Some of the techniques are highly accurate but they require lots of production data, hence, their applicability early in the reservoir life is restricted. Others could be applied earlier, but on the other hand, they have very low accuracy.In this paper ten parameters (original oil in place, asset area, net pay, initial reservoir pressure, porosity, permeability, Lorenz coefficient, API gravity, initial water saturation and oil viscosity), which are usually available early in the life of the reservoir, are used to estimate the oil recovery factor through application of four Artificial Intelligence (AI) techniques namely: artificial neuron networks (ANNs), Radial Basis Neuron Network (RNN), ANFIS-2 (Adaptive Neuro Fuzzy Inference System, Subtractive Clustering), and SVM (Support Vector Machines). Data from 130 sandstone reservoirs were used to learn the AI models, and then an empirical correlation was developed based on the ANN model. The suggested AI models and the developed ANN-based correlation were then tested in other 38 sandstone reservoirs.The results obtained showed that ANN-based correlation successfully predicted the recovery factor based on early time data only with absolute average percentage error (AAPE) of 7.92%, coefficient of determination (R2) of 0.9417, root mean square error (RMSE) and maximum absolute percent error (MAE) of 3.74 and 24.07%, respectively. ANN-based empirical correlation over-performed RNN, ANFIS-2, and SVM models in term of AAPE, MAE, and RMSE for testing set. Comparison of the recovery factor predicted by the developed equation with three available correlations showed that the developed equation predictability is about 5 times better that the most accurate correlation (of the currently available ones) in term of AAPE for predicted RF of the tested 38 reservoirs.
Estimation of Geomechanical Failure Parameters from Well Logs Using Artificial Intelligence Techniques
Authors: Alloush R. M. , Elkatatny S. M. , Mahmoud M. A. , Moussa T. M. , Ali A. Z. , Abdulraheem A.. 
Published: October 2017

Abstract
Geomechanical failure parameters are best estimated from core samples, which are not always available. To address this, numerous relationships have been proposed that relate rock strength to parameters measurable with geophysical well logs, but they tend to have limited accuracy or low range of applicability. This paper aims at overcoming the limitations of the existing techniques by using different artificial intelligence techniques (AI).
The data used in this study are usually available from the commonly conducted well log surveys (neutron porosity, gamma ray and bulk density) as well as the sonic log. Data sets from 10 wells in carbonate formations and 30 core samples were used to build a simple, yet powerful, model that can predict the rock failure parameters from well logs. The derived model is working because it considers many factors that affect both the rock strength and elastic moduli.
Three artificial intelligence techniques; Artificial Neural Networks (ANNs), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Support Vector Machines (SVM) were used to predict three geomechanical failure parameters, namely: the angle of internal friction (friction angle or FANG), unconfined compressive strength (UCS) and tensile strength (TSTR). For each parameter, the three AI techniques were implemented to train AI models and then unseen data was used to validate the models.
From the three tested AI techniques, ANN model gave the best results and out-performed the other models obtained using ANFIS and SVM for the FANG prediction, with correlation coefficient of 0.98 and AAPE around 5.8%. The ANN model was programmed in a (white box) manner, which allowed the successful extraction of the weights and biases from the network to be easily used. For TSTR and UCS prediction, ANFIS models performed better than ANN and SVM. The two ANFIS models had correlation coefficients of 0.99 and Average Absolute Percentage Errors (AAPE) of 4.9% and 3.7% respectively.