bluuebunny/crossref_metadata_2025_split huggingface dataset:
An Intelligent Functional Network Approach to Develop Mathematical Model to Predict Sonic Waves Travel Time for Carbonate Rocks
Authors: Tariq Zeeshan 
Published: October 2018

Abstract
Compressional and shear wave travel times are used to determine rock dynamic elastic parameters. Elastic parameters are used in obtaining the in-situ stresses of the rock, calculate safe mud weight, avoid wellbore instability problems and design optimum hydraulic fracture geometry. Often overlooked, weakness in the calculation of elastic parameters studies is a major cause of well-bore problems and unsuccessful well stimulation jobs. In well logging, compressional and shear wave travel time logs are one of the common logs to be recoreded in almost every formation. In cases when the sonic logs are missing, these logs values are estimated using empirical correlations. So far, none of the available models or correlations are universally accepted by log analysts since every model estimates the log values significantly different from the others, which may be suitable for one reservoir and not for other. As a result, inaccurate values can potentially raise major concerns throughout the life of the well. The error in sonic log values affects the elastic parameters, which can result in either underestimation or overestimation of the in-situ stresses.
So, the objective of this paper is to develop robust empirical model for compressional and shear wave travel times using different functional network approach. For this purpose, well logs data, such as; gamma ray, bulk density, and neutron porosity were collected from different wells which are drilled in a carbonate formation. Functional network tool is used to develop mathematical model for compressional and shear wave travel times. This AI model were trained on the data of several well and validated on the remaining wells. Different types of functional network tools were studied, namely; forward selection model FNFSM, backward elimination model FNBEM, forward backward model FNFBM and backward forward model FNBFM. Then based on correlation coefficient R and average absolute percentage error AAPE, a comparison was made and the one which performed better was selected for prediction of sonic wave transit times.
The novelty of this work is that for the first time, the explicit mathematical model is developed from functional network to estimate compressional and shear wave travel time. The mathematical model developed from the functional network is easier to implement. The new model will eliminate the need to use expensive software to run AI model. The new model for sonic transit time significantly reduces the uncertainties in calculating the sonic transit times in carbonate formation.
A New Approach to Characterize CO2 Flooding Utilizing Artificial Intelligence Techniques
Authors: Hassan Amjed , Elkatatny Salaheldin , Mahmoud Mohamed , Abdulraheem Abdulazeez 
Published: October 2018

Abstract
The use of carbon dioxide in miscible flooding has been considered as one of the most effective techniques for enhancing oil production. The flooding efficiency is an extreme function of the minimum miscibility pressure (MMP), therefore, searching for a quick and rigorous method to determine MMP is highly needed. Slim tube experiments are normally used to measure the minimum miscibility pressure. However, such experiments are time-consuming and very costly. Different correlations have been developed to determine the MMP during CO2 injection process. These empirical equations are not widely applicable and might produce severe estimation errors, because they are developed based on limited experimental results.
This paper proposes a new technique to evaluate the CO2 flooding and minimize the uncertainties of using numerical approaches. The objective of this work is developing a reliable model to predict the MMP during CO2 flooding. Actual case studies for flooding heterogeneous and anisotropic reservoir were utilized to generate the MMP model, more than 140 data points were used to construct and evaluate the proposed model. Several artificial intelligence techniques were studied to estimate the CO2−MMP for a wider range of conditions. The developed models investigate the effect of API gravity, fluid composition, and injected gas composition on the performance of CO2 flooding operation.
The CO2−MMP was estimated using different artificial intelligence techniques including; radial basis function network, artificial neural network, generalized neural network and adaptive neuro-fuzzy inference system. The wellbore condition and reservoir parameters were used to provide an accurate and quick prediction for the flooding performance. Sensitivity study was conducted to optimize the model parameters. Then, the optimized artificial neural network model was utilized to extract an empirical equation.
The developed equation was verified using actual field data an acceptable average absolute percentage error (AAPE) of 6.6% was obtained. In addition, the developed CO2−MMP model was compared with different determination approaches. It is found that, the proposed technique outperforms the current CO2−MMP models. This work would afford an effective approach to characterize the CO2−flooding for complex reservoirs, also improve the prediction performance of commercial software, which leads to a better production management in the particular CO2−operations.
Energized Fluids for Upstream Production Enhancement: A Review
Authors: Karadkar Prasad , Bataweel Mohammed , Bulekbay Aslan , Alshaikh Abrar A. 
Published: October 2018

Abstract
Energized fluids are defined as fluids with one or more compressible gas components, such as CO2, N2, or any combination of gases, dispersed in a small volume of liquid. Generally, these fluids offer an attractive alternative to conventional stimulation fluids in many cases such as low reservoir pressure, water-sensitive formations, and/or the need for shorten flowback period. Energized fluids have many challenges such as low stability at high temperature, high friction pressure during pumping, corrosion in the case of using CO2, and the need for specialized surface pumping equipment. The objective of this paper is to describe the typical components of energized fluids and their effect on the fluid performance. Also, lab testing methods used to evaluate energized fluids performance will be discussed in detail.
Foam is a class of energized fluid used for different applications including acidizing, hydraulic fracturing, and fluid diversion. For each application, foam should have a minimum acceptable value of viscosity, stability, and/or fluid compatibility. Those values were reviewed from literature and categorized based on reservoir conditions. Also, different rheological models are analyzed to understand foam flow behavior in both tubing and porous media. Finally, the mechanism of foam transport in porous media is reviewed in this report, which gives insight into foam stability and propagation.
The most common application of nitrogen is in artificial lifting, while supercritical CO2 is proposed for condensate banking removal. Selection of the right surfactant, like alpha olefin sulfonates, which are thermally more stable than alkyl ether sulfates, is crucial while designing foam treatment, as they produce the most persistent foams at high salinity and elevated temperatures in the presence of synthetic and crude oils. Currently available foam-based fracturing fluid systems in the industry have temperature limitations to 300°F. The crosslinked gelled foam has a better temperature range than the viscoelastic foam fluid system, whereas non-crosslinked biopolymer-based foam fluid showed better proppant pack cleanup characteristics. In a recent report, the addition of 0.1% silica nanoparticles along with cationic surfactant was shown to enhance CO2 foam stability by 13 hours. In this review, all these aspects of energized fluids are well reported from literature.
In this paper, we discuss findings from different lab testing and field demonstration of energized fluids. Compositional modelling for hydraulic fracturing with energized fluids is also reviewed to add insight on fracture geometry estimation. This paper provides guidelines and recommendations for selecting the right energized fluids for successful stimulation treatment.
Deep Gas Drilling Optimization in the lateral section in middle east
Authors: Balka Muhammad Sarfraz , Ghazzawi Ayman , Cui Shuai , Fuenmayor Luis 
Published: October 2018

Abstract
Deep gas drilling in middle east is very challenging and this becomes even a bigger challenge when the wells are drilled horizontally and in 5 7/8" hole size. Two types of rocks are drilled across different field. First one is khuff formation in upper Permian and second is Unayzah formation in lower Permian. Both formation have different challenges, this paper is focused on optimization in Khuff reservoir where the service provider is involved. Deep gas drilling optimization was based adding new technical experts and peer reviewing the learning in place and implementing the new best practices. Historically plenty of efforts were made for performance improvement, however still there was and is room for continuous improvement. All the efforts were based on the implementation technologies, pushing the technology to limit, improving the knowledge of drilling environment and continuous changes in the drilling strategies. After reviewing the following changes were recommended and are in place across different fields
Connection practice - reducing the overall time by minimizing the reaming time. BHA stabilization – changing the stabilizer position and maximum OD of the stabilizer. Performance comparison between Standalone and powered RSS and implementation. No Wiper Trip till TD, Trip only required for BHA change or other operational requirement.
After implementation, ratio for number of wells have been drilled in one run is increasing. Drilling parameters implementation has resulted in consistent ROP and BHA optimization has resulted in reduction in LIH year on year.
An innovative Approach Using Micro-Emulsion Treatment Techniques to Remove Sandstone Formation Damage: Lab Testing and Field Application
Authors: Almajid Hussain A. , Shuwaikhat Hisham I. , Abou Zeid Saleh M. , Addagalla Ajay Kumar 
Published: October 2018

Abstract
Matrix stimulation treatments executed with coiled tubing (CT) face various challenges in terms of design, execution, and evaluation. The design phase typically relies on information that is usually poorly known (e.g., extent of damage). Treatment pumping schedules and fluid concentrations are often determined based on previous experience and accepted local practices. In most cases, tools like high pressure differential jetting nozzles are used to provide deeper penetration and lower break-out pressures. The depths at which those tools are operated usually depend on a prior log interpretation. Finally, treatment evaluation is typically limited to the comparison of pre and post-stimulation wellhead pressures and rates.
Over the past decade, numerous sandstone stimulation treatment alternatives have been introduced to the industry to address formation damage related issues. Yet, the same question often remains: has the design been effectively executed and was the intervention successful?
A CT downhole measurement system which consists of downhole gauges providing real-time data of pressure, temperature, gamma ray, and casing collar locator, has proven to be a game-changing technology with respect to treatment execution, improving both intervention efficiency and safety. The downhole measurements, along with the possibility to acquire distributed temperature & pressure (TPD) surveys, have also shown to be the most effective solution for treatment evaluation to date.
This paper will describe a case study of how the system can be used to optimize the acidizing treatment of damaged wells and ensure its effective stimulation. It will also demonstrate how the real-time data analysis during the intervention process can result in high success rate of such intervention jobs.
Surface and Interfacial Tension Behavior in the Use of Ionic Liquids as Additives for Surfactant-based Enhanced Oil Recovery
Authors: Hanamertani Alvinda Sri , Pilus Rashidah M. , Manan Ninie A. , Ahmed Shehzad 
Published: October 2018

Abstract
Due to the favorable properties and versatility, the applications of ionic liquids (ILs) have been introduced in petroleum industry for different purposes including Enhanced Oil Recovery (EOR) processes. The ability of ILs to alter surface behavior of surfactant molecules by producing specific interactions with surfactant has motivated further investigation on its application to the important chemical EOR mechanisms, such as surface and interfacial tension lowering under reservoir conditions. The mixtures of in-house surfactant and different types of ILs (imidazolium- and eutectic-based ILs) as additives were formulated using different concentration ratio and prepared in fixed brine salinity. The surface tension (ST) and interfacial tension (IFT) behavior of the formulated mixtures were investigated by performing pendant and spinning drop tests at ambient and high temperature. In general, the extent of ST and IFT reduction was dependent on the type and concentration of ILs. The presence of ILs was able to lower both ST and IFT of surfactant solution and its effect as a function of ILs concentration was found to be more pronounced at high temperature. Of all ILs, eutectic-based IL showed the highest ability to decrease ST at any concentration used. Similarly, the eutectic-based IL was also regarded as the best ILs in lowering the surfactant solution/oil IFT even in the use of low concentration. From this study, it can be concluded that ILs can be proposed as new additives to alter the surface and interfacial behavior which are taking place through some surfactant-IL interactions and eventually improve surfactant performance in reducing ST and IFT. Surfactant solution with a lower ST due to the addition of ILs is expected to provide promising performance for surfactant-based EOR process involving gas mobility control application leading to enhancement of sweep efficiency, while a lower IFT value can provide favorable conditions for better oil displacement efficiency.
Machine Learning and Natural Language Processing for Automated Analysis of Drilling and Completion Data
Authors: Castiñeira David , Toronyi Robert , Saleri Nansen 
Published: October 2018

Abstract
During Drilling and Completion (D&C) operations large volumes of data are typically collected in oil and gas fields. These datasets typically contain hidden (valuable) information that could be used to improve D&C performance (e.g., by identifying bottlenecks in drilling operations, analyzing non-productive time, optimizing rig schedule based on key indicators, etc.). Unfortunately, D&C datasets are typically not well suited for data mining: they are not structured, they are text heavy and they often contain numerous gaps and errors that hinder automated pre-processing techniques. In this paper, an innovative method to automatically extract smart analytics and opportunities from D&C reports is presented.
Initially, a combination of Natural Language Processing, Data Mining, and Machine Learning algorithms are used to quality check a large volume of drilling data (including the text in the daily drilling reports), extract crucial information, and predict the non-productive time and its type. This results in a significant reduction of the labor-intensive quality check task for thousands of datasets and also the unbiased classification of the events. Then, the D&C datasets are integrated with other data sources such as production, geology, reservoir, etc. to generate a set of crucial drilling and reservoir management metrics.
The proposed method, which was successfully applied to fields in North and South America, is applied here to two onshore fields located in the Middle East. By applying the developed tool, the data processing and integration time that used to take months to accomplish could be reduced to only a few days. In addition, analyzing metrics such as the Drilling Efficiency Index, normalized drilling days for each field and well type, cost analysis, detailed analysis of non-productive time, effect of completion parameters on production, design efficiency, etc. enabled us to quickly identify the D&C bottlenecks in each field and provide customized solutions to diagnose each problem. In addition, the historical data was used to improve future rig scheduling and resource allocation by applying advanced optimization algorithms with cumulative oil production, net present value, and operation time as the objective functions. In the final stage, the results were vetted by the experts to assure it meet the best D&C practices. The novelty of the presented method lies in using advanced technologies such as Natural Language Processing, Data Mining and Machine learning to QC, mine, integrate and analyze large volumes of D&C data in a very short time, find the bottlenecks and optimize the future plan with evident benefits of improving D&C performance and capital efficiency from a global reservoir management perspective.
Predicting the Optimum Concentration of Partially Hydrolyzed Polyacrylamide Polymer in Brine Solutions for Better Oil Recovery, Experimental Study
Authors: Khamis Mohammed A. , Omer Omer A. , Kinawy Mostafa M. 
Published: October 2018

Abstract
Polymer injection is one of the most applications used in enhanced oil recovery. In order to achieve better sweeping efficiency, polymer is added to the injected water to increase its viscosity and ultimately minimize the viscous fingering. Partially hydrolyzed polyacrylamide (HPAM) is one of the polyacrylamide groups. It is characterized by the shape of straight chain polymer of acrylamide monomers in which some of it has been hydrolyzed. The viscosity of the polymer solution should be maintained in order to keep better sweep efficiency. Salinity, polymer concentration and temperature are the most important factors that strongly affect the polymer viscosity and behavior. The aim objective of this study is to estimate the optimum polymer concentration based on the suitable polymer solution viscosity. This viscosity will be suitable for preferable mobility ratio. In this work, five groups of polymer solutions were prepared with different salinities. In each group, several polymer solutions were prepared at different polymer concentration. The viscosity of prepared solutions was measured at different temperatures. New models correlate the flow behavior index (n) and flow consistency index (k) with polymer salinity, polymer concentration and temperature were developed and validated using the experimental data. In order to estimate the required optimum polymer concentration, the developed models were combined with other developed models that describe the polymer shear rate in porous media. Results show that, the cross plots of the measured and corresponded calculated (n) and (k) values indicated better prediction compared with the latest published correlations with a squared correlation coefficient of 0.9912 and 0.9962 respectively.
In addition, the calculated average relative error of the predicted (n) and (k) were 0.97 % and 6.07% correspondingly. Using these models, the power law viscosity equation and shear rate in porous media equation, the required polymer concentration can be estimated with high accuracy. Consequently the required optimum polymer concentration needed to achieve better sweeping efficiency can be determined and therefore enhance the oil recovery. This technique will save time and effort spent to find the optimum polymer concentration using the traditional methods.
Conformance Improvement in Oil Reservoirs by Use of Microemulsions
Authors: Torrealba V. A. , Hoteit H. King 
Published: October 2018

Abstract
The performance of many improved and enhanced oil recovery techniques in conventional reservoirs is frequently degraded by conformance problems. The presence of high permeability streaks or thief layers between injection and production wells typically results in pre-mature water breakthrough, high water cut and deficient volumetric sweep. As a result, significant oil volumes in the reservoir may not be contacted by the injection fluid.
Several conformance improvement techniques (e.g. foams, gels, resins) have been developed and practiced in improved oil recovery operations. Each technique has its own advantages and limitations related to deployment practicality, effectiveness, and durability. In this paper, we introduce a novel conformance improvement method (CIM) that we consider to be practical, effective and durable. The CIM process consists of cyclical injections of pulse slugs of surfactant alternating with brine. The surfactant slug compositions are selected based on the rheological behavior of the microemulsion phase. The chemical slugs are configured such that the viscosity of the injected fluids is kept low to preserve injectivity and to ensure invasion of the conformance agent towards the thief zones. The trailing brine slugs are designed to produce a high-viscosity microemulsion as they mix with the leading surfactant slugs in the reservoir. The proposed process leads to a reduction in the effective mobility of the fluids in the thief layers. As a result, the chase waterflood would divert into previously uncontacted layers to improve sweep efficiency.
The potential of the proposed CIM in improving oil recovery is demonstrated by various simulations of reservoir cases under waterflooding. We performed various sensitivities to investigate the effectiveness of the proposed process that include well-spacing, permeability contrast, size of the thief layers, heterogeneity, and size of the chemical pulse slugs. Simulations showed that this process is effective in addressing reservoir conformance issues, and therefore it has the potential to improve sweep efficiency and recovery factor in reservoir with distinct thief layers. The treatment surfactant volumes are relatively small, which enables this process to be cost-effective.