bluuebunny/crossref_metadata_2025_split huggingface dataset:
Unleashing the Limitation of Extended-Reach Wells; a Flow Optimization Well Architecture
Authors: Al Qahtani Abdullah M. 
Published: October 2018

Abstract
Technical and practical hurdles were behind the limitation of drilling extended reach wells. These include, pipe torque and drag, fluid circulation control, and directional control of drilling such wells. From practical point, horizontal and extended-reach wells are proved to have a finite-conductivity wellbores, which means that there will be a flow capacity limitation when reaching certain length of wellbore. That implies that the flow optimization consideration may limit the drilling requirement to the effective wellbore length. To maximize the extension of drilling, whilst unleashing the limitation of wellbore finite conductivity, a U-shaped horizontal well design is investigated in this study. The U-shaped well architecture is new but was not studied from the flow capacity drivers, was hence was not considered as a solution for optimizing the flow in extended reach wells. This is to unleash restriction imposed by the finite conductivity of the wellbore and hence maximize flow capacity and sweep efficiency.
A semi-analytical model was developed to validate results from pressure transient analysis and wellbore flow simulation. The model was validated with data in literature for a single horizontal well. The flow simulation of the connected wells showed a big increase in flow capacity. In addition, the new well architecture can be utilized to optimize flow by altering the flow rate from both ends. This solution can be used as a solution to avoid the used of downhole chokes to restrict flow. In addition, it can be used to optimize sweep efficiency as extended reach wells cross long section in the formation.
Field Investigation on the Use of Surface Data for Pressure Transient Testing
Authors: Al Qahtani Abdulla 
Published: October 2018

Abstract
Pressure transient testing have been routinely relying on the downhole recorded pressure data. Acquiring such data require special operation that enables well intervention to place pressure gauges close to the producing formation. A side from the associated cost, some wells are difficult to be tested for remoteness, or restricted entry.
Modest attempts of using surface pressure data for analysis of pressure transient test have been documented. This is due to the fact that wellhead pressure data are associated with significant drop in pressure and temperature during the production and shut-in times. This will affect the ambient conditions at which data are acquired. In some conditions, this will impact the accuracy and stability of data collected. In addition, it is impacted by the flow regimes in wellbore during the production and shut-in times. Surface pressure data can also be affected by the presence of significant phase redistribution in the wellbore during the shut-in period.
This paper presents actual field examples demonstrating the practicality and field application of measured surface data in pressure transient tests. The data were collected using high accuracy pressure gauges both surface and downhole. The test data were analyzed for both conditions. The data covers water injection wells, oil wells with different API gravity values. The presented work revealed the merit of using the readily available data to provide on-demand answer with excellent accuracy that maybe the optimum solution for digital oil fields. This approach does not rely of calculating the bottom hole pressure from surface pressure but rather use direct measurement of surface pressure differential. The study was also to find the optimum conditions that such solution will provide a substitute for downhole data acquisition. The solution best fit in operating condition where the flow is homogenized during flow condition with less possibility of liquid drop outs during static data acquisition. Such shortcomings are easily overcome by using the whole model solution with modern diagnostic pressure transient software's. Several field cases were presented that substantiated the fact that the surface data transient pressure is a viable solution.
Machine Learning Derived Correlation to Determine Water Saturation in Complex Lithologies
Authors: Khan Mohammad Rasheed , Tariq Zeeshan , Abdulraheem Abdulazeez 
Published: October 2018

Abstract
Possibly the most underrated petrophysical parameter, the importance of water saturation cannot be emphasized enough with a whole range of petrophysical as well as reservoir engineering computations being dependent on its accurate determination leading to vital field development decisions; reserves estimation, waterflooding efficiency calculation and capillary pressure deduction. In 1942 Archie was first to present the equation to determine water saturation in a clean, non-clay reservoir. Ironically, ever since decades have passed with the intricacy of water saturation determination yet to be untangled in complex lithologies, especially in carbonates. Several researchers have tried to deconvolute the water distribution in composite formations by formulating empirical correlations that depend on log derived data which is not a very precise representation and hence no consensus exists among log analysts about which model can be universally used.
The use of computer generated algorithms, fuzzy logic and neural networking is picking up pace in the petroleum industry. Consequently, in this paper we show how Machine Learning can be used to generate a correlation, to determine water saturation in carbonate reservoirs, which is simple and practical to use in the sense that has less uncertainty in the parameters that it employs compared to existing models. In this work, multiple machine learning techniques namely, Artificial Neural Network (ANN), and Adaptive Neuro-Fuzzy Inference System (ANFIS) are used to estimate water saturation using conventional wireline log data as input parameters and the output is core dean-stark data. The data comprised of more than 2000 well log points which were reduced to around 150 corresponding to available core data. All the developed models are compared after a rigorous sensitivity analysis based on various artificial intelligence algorithms.
This work clearly shows that computer-based machine learning techniques can determine water saturation with a precision of approximately 94% when related to experimental core values. The developed correlation works extremely well in prediction mode with the shale affected log data as inputs. A comprehensive numerical and illustrative evaluation of the claimed accuracy is shown along with the error analysis between both the machine learning techniques used.
Enhancement of challenging prestack land data for improved processing and imaging
Authors: Bakulin Andrey , Dmitriev Maxim , Silvestrov Ilya 
Published: October 2018

Abstract
Modern seismic acquisition is trending toward recording high-channel count data with smaller field arrays or single sensors. Reducing the size of field arrays leads to a deterioration of data quality. Many processing steps requiring estimation of prestack parameters become more challenging due to the low signal-to-noise ratio (SNR) of the data. Conventional processing algorithms require estimation of velocities, statics, and surface-consistent scalars and deconvolution operators, and need good prestack data quality. This is rarely the case for land seismic data acquired in arid desert environments of Saudi Arabia with a complex near surface. We present two methods for prestack seismic signal enhancement based on utilization of neighboring traces. The first method, called supergrouping, performs local summation of traces using a global normal moveout correction to align reflected signals. The second approach, called nonlinear beamforming (NLBF), is a data-driven procedure for estimating local moveout directly from the data. We demonstrate the signal enhancement ability of these procedures on synthetic and challenging land seismic data from Saudi Arabia. We show that application of supergrouping and nonlinear beam forming (NLBF) provides significant uplift for various steps of land seismic processing such as deconvolution, estimation of statics, first-break picking, full-waveform inversion (FWI), and imaging.
Prediction of Rate of Penetration of Deep and Tight Formation Using Support Vector Machine
Authors: Abdulmalek Ahmed S , Salaheldin Elkatatny , Abdulazeez Abdulraheem , Mohammed Mahmoud , Abdulwahab Z. Ali , Mohamed I. M. 
Published: October 2018

Abstract
Rate of penetration (ROP) is the main function that affects drilling operation economically and efficiently. Many theoretical models reported in the literature were produced to predict ROP based on different parameters. Most of these models used only drilling parameters to estimate ROP. Few models have considered the effects of drilling fluid on ROP using a simulated data or a few real field data. Some of the researchers used artificial intelligence to predict ROP by only one method.
The objective of this research is to predict ROP based on both drilling parameters and mud properties such as weight on bit (WOB), rotary speed (RPM), pump flow rate (Q), standpipe pressure (SPP), drilling torque (τ), mud density (MW), plastic viscosity (PV), funnel viscosity (FV), yield point (YP) and solid (%). More than 400 real field data in shale formation are used to predict ROP using support vector machine (SVM) which is a method of artificial intelligence (AI) and compare it with different mathematical models.
The result showed that support vector machine (SVM) technique outperformed all the theoretical equations of ROP by a high margin as shown by a very high correlation coefficient (CC) of 0.997 and a very low average absolute percentage error (AAPE) of 2.83%.
Invisible Lost Time measurement and reduction contributes to optimizing total well time by improving ROP and reducing flat time
Authors: Ouahrani Louanas , Haris Ahmad Nahil , Suluru Srinivasulu , Chiha Arfa , Al Fakih Abdulqawi 
Published: October 2018

Abstract
Optimizing well time and its expenditures has become a necessity for which all drilling operators and service companies are challenged on. This requires a combination of well engineering and operations efforts from avoidance of service quality issues that lead to Non-Productive Time (NPT) to the reduction or elimination of Invisible Lost Time (ILT). Traditionally, more focus and efforts are concentrated around the NPT avoidance, as its impact is visible and has a huge and direct financial consequences that leads to increasing well cost. Most oil & gas operators and services companies have as NPTr (NPT rate per 1000 operating hours) one of their main KPIs (Key Performance Indicators) to measure their performance and ranking.
The Invisible Lost Time (ILT) analyses is considered another area of focus where the drilling performance can make a big step change in the way how well operation activities are performed. This doesn't only require the identification and measurement of the ILT impact, but also taking the appropriate measures to reduce and eliminate it from both drilling time and flat time operations. This is essential to remove the variability and bring consistency to the well operations performance among all the rigs and projects.
The approach described in this paper involves the implementation of a tracking tool that uses the rig surface real time data that will be referred as High Frequency data (HF), combined with the Daily Operation Reports, referred as Low Frequency data (LF) to track and display multi-well drilling operation activities comparison (drilling and flat time). This allows to identify any variation and to measure the Invisible Lost Time (ILT) within the different drilling activities and rigs compared to the best and to the benchmarks. Once the ILT is identified and measured, the drilling engineering and operation teams take appropriate actions using LEAN principles and methodology to tackle the inefficiencies and initiate performance improvement opportunities. These are then prioritized based on its impact and assigned as discrete projects for rig and office based teams to address the gaps. Measuring is the first step that leads to control and eventually to improvement. "If you can't measure something, you can't understand it. If you can't understand it, you can't control it. If you can't control it, you can't improve it". (H. James Harrington).
Estimation of Typical Total Dissolved Solids in Water from Downhole Ultrasonic Sound Speed Formation Tester Survey
Authors: Torlov Vladislav S. , Akinyose Olusegun M. , Amin Asif 
Published: October 2018

Abstract
Surface laboratory analyses of downhole water samples can typically introduce a significant delay between the sampling and reporting of fluid properties. The work described in this paper aims to quantify downhole formation water property measurements, and derivations from the high frequency acoustic sensor data acquired in water-based mud settings. The methodology is implemented by a careful estimation of the Total Dissolved Solids (TDS) typically found in formation waters.
Wireline formation testing and sampling tools are used to collect downhole samples, and to monitor cleanup from mud filtrate to collect a representative formation fluid sample with the aid of different downhole sensors, particularly optical spectrometers. In case of water-based mud (WBM) and water cleanup, the optical spectrometer does not distinguish between filtrate and formation water due to the very low optical spectrum contrast. Oftentimes, non-calibrated resistivity sensors are used to differentiate filtrate from formation or injection water, when there is a detectable salinity contrast. Resistivity and density trends are successfully utilized for cleanup monitoring, however the measured values could lack accuracy to be reliably used as the input parameters in petrophysical evaluation, particularly in the cases of transition zones characterization.
The objective of this work is to verify estimation of the common TDS from downhole ultrasonic sound speed sensor data, acquired by wireline formation tester at in-situ conditions, with the laboratory reported values; and to demonstrate its applicability for transition zones testing. Comparison of laboratory reported TDS was made for water samples collected from various reservoirs, borehole conditions, and a high range of water composition. Considering the different methodologies and consistent trends observed, the new real-time monitoring solution adds a confident tool for quantitative fluid properties assessment, and particularly in a challenging transition intervals. Based on different methodologies, the relationship between salinity, concentration of solids and density was evaluated.
The work provides the methodology to estimate TDS of water in-situ to characterize and differentiate water types. In combination with real-time density measurement, confidence in fluids properties assessment is increased, assisting in swift reservoir evaluation and decision making for prospecting and operational purposes.
Experimental Verification of a New Approach to Long-Range EM Imaging
Authors: Schmidt Howard K. , Servin Jesus Manuel , Ellis Erika Shoemaker 
Published: October 2018

Abstract
Proximity Sensing was recently proposed as way to simultaneously increase both range and resolution in cross-well EM tomography. The approach is applicable to reservoirs with resistive seals. Earlier reports were based on Finite Element Models (FEM) of layered structures, with dielectric and conductivity contrasts matching those of known reservoirs.
Experimental work, now reported, is consistent with expectations based on FEM simulations. Synthetic layered structures have been investigated using a 1.3 GHz Ground Penetrating Radar (GPR) system. Scaled reservoir model was constructed in a one-meter tank comprising sand with filled with fluids of variable dielectric constant and conductivity. In this system, dry sand, brine-saturated sand and a polymer foam provide a useful mimic for the electrical properties expected for a carbonate reservoir sealed by anhydrite. Water saturated porous media served as model bounding layers in analogy to known geologic structures. Data were recorded in the time domain using EM transients. Observed trends in velocities and amplitude shifts were consistent with FEM models. Interestingly, polarization dependent signal transport first indicated by FEM modeling was supported by these experimental results.
Results to date indicate that greatly increased EM propagation can be achieved through resistive geologic layers than directly through relatively conductive reservoir media. We confirm that these layers act as planar transmission lines and not as waveguides – meaning that there is no hard lower cutoff frequency and longer wavelengths can be used to sense and characterize reservoir fluids proximal to the dielectric channel. The results also confirm that variations in bounding layers modulate the amplitude and velocity of the signal in the dielectric channel and thereby demonstrate concept of Proximity Sensing.
These results support a new technical direction for EM characterization of reservoirs, especially in conjunction with magnetic contrast agents, enabling efficient localization of by-passed oil and mapping remaining oil columns in mature reservoirs.
Prediction of Optimum Injection Rate for Carbonate Acidizing Using Machine Learning
Authors: Sidaoui Ziad , Abdulraheem Abdulazeez , Abbad Mustapha 
Published: October 2018

Abstract
In stimulation, acid is pumped into the formation at a certain rate to penetrate deep into the formation. The optimum injection rate is one of the of the key parameters that is sought after. By definition, the optimum injection rate happens when the acid penetrates deep with the minimum volume. In a linear coreflood experiment, this translates into a dominant wormhole dissolution pattern. Outside the range of such rate, the pattern could be face dissolution, conical wormhole, ramified wormhole, or uniform dissolution. Researchers have been trying to develop models to predict the optimum injection rate. However, such models require some reaction kinetic parameters to obtain the optimal injection rate.
This paper aims to investigate the dependence of different parameters on the optimum injection rate for matrix stimulation. This study incorporates a wide range of data points obtained from linear coreflood and develops a new, robust, easy-to-use model using machine learning.
This model predicts the optimal injection rate with the help of three arguments, namely, rock properties, HCl properties, and experimental conditions. The data used in this study are composed of 170 experimental data points. The data were collected from Indiana, Desert Rose, and Lavoux limestone. The results show that the newly developed model is capable of predicting the pore-volume-to-breakthrough (PVBT) along with the optimal injection rate with 90% accuracy. Furthermore, the model only requires the following parameters: type, length, diameter, porosity, and permeability of the core; HCl strength; temperature; and pressure. Such model will narrow the search for the optimal injection rate and will significantly reduce the number of laboratory experiments needed to obtain the PVBT curve. Furthermore, the results from this model can be upscaled to field scale.
The novelty of this work lies in providing a simple, yet powerful, tool that is easy to use to predict the optimum injection rate. This will help production engineers to efficiently design a successful acid job.
New Technique to Estimate Temperature Distribution During Thermal EOR Operations
Authors: Hassan Amjed , Elkatatny Salaheldin , Mahmoud Mohamed , Abdulraheem Abdulazeez , Hossain Enamul 
Published: October 2018

Abstract
Thermal operations are the most effective enhanced oil recovery (EOR) techniques used to increase hydrocarbon production, especially in heavy oil reservoirs. Thermal EOR involves injecting of steam or hot fluids into underground reservoirs to alter the physical properties, specifically, the fluid viscosity and effective mobility. Numbers of mathematical (numerical or analytical) models were developed to estimate the temperature variations during such operations, however, those models assume constant fluid velocity throughout the reservoir, then, severe estimations errors could be generated. The objective of this paper is to develop a new approach for determining the temperature distribution during thermal-EOR processes, and the heat propagations with time and distance from the wellbore with acceptable tolerance.
The main aim of this work is to develop a reliable model to predict the temperature distributions in porous rocks during thermal EOR operations. Artificial intelligence (AI) methods were utilized to compute the temperature profiles, those models would minimize the complexity and uncertainties of numerical approaches. The impact of formation permeability, injection time and distance from wellbore were considered to develop effective models. To ensure a high level of model reliability, more than 220 data set was used for training and testing the proposed models.
Temperature distribution was determined using the neural network, fuzzy logic system, and generalized intelligent networks. Different model's parameters were used to optimize the intelligent networks, average absolute error and correlation coefficient were utilized to measure the model performance. ANN model showed the best prediction performance, an average absolute error of 6.2 % and a correlation coefficient of 0.98 was obtained using unseen data set.
Comparing 5-Different Artificial Intelligence Techniques to Predict Z-factor
Authors: Salem Adel , Elgibaly A. , Attia M. , Abdulraheem Abdulazeez 
Published: October 2018

Abstract
Gas compressibility factor plays an important role in reservoir engineering applications. A lot of techniques have been proposed to predict Z-factor. Standing-Katz (S-K) Z-factor chart is the most common and popular among them and is being used since 1941. Many correlations have been proposed after S-K chart to regenerate and increase its range in an accurate manner. Some of these models are direct models such as Papp Correlation, Shell Oil Company Correlation, and Beggs and Brill Correlation, others are indirect correlations such as Hall-Yarborough and Dranchuk-Abu-Kassem Correlation.
In this study, five different artificial intelligence techniques are implemented to predict Z-factor. These techniques are neural network, radial basis function network, fuzzy logic, functional network, and support vector machine. To build and test these techniques, Standing-Katz charts data was used in which about 70% of the data was used for training and 30% for testing.
Results from this work show that artificial intelligence techniques can predict Z-factor with low error such as Neural network, Radial basis function, Fuzzy logic, and Support vector machine. Neural network is the best technique among others in predicting Z-factor.
This work will help in selecting the best artificial intelligence technique for predicting Z-factor.