bluuebunny/crossref_metadata_2025 huggingface dataset:
Physico-Mathematical Model for forecasting Gas-Condensate Reservoir Performance in Multiphase Linear and Radial Flow Systems
Authors: Abdul-Latif Benson Lamidi , Abdul-Rashid Bawah
Published: October 2018

In this paper, a physico-mathematical model is presented for forecasting gas condensate reservoir performance in multiphase radial and linear flow systems using analytical and semi-analytical models. The proposed methodology is based on the three phase black-oil formulation, incorporating capillary pressure effect in fluid PVT properties in linear and radial flow regimes under constant bottomhole pressure (BHP) and constant flow rate well conditions.

The proposed analytical methodology is developed by reducing a system of governing partial differential equations (obtained by rewriting the black-oil mathematical formulation using the Boltzmann transformation) to a system of ordinary differential equations and hence solved using an adaptive step-size Runge-Kutta integration technique coupled with the shooting method without the need to linearize the system of differential equations.

Simultaneously, the proposed physico-mathematical model forecasts reservoir pressure profile and saturation behavior in linear and radial multiphase flow systems using three phase relative permeability data and gas condensate PVT data without the need for pressure-saturation or producing gas-oil ratio data inputs into the model.

To validate the proposed semi-analytical solution for multiphase-constant gas flow radial systems above the dewpoint pressure, analytical solutions were compared to the line-source solution.
Optimized Drilling Fluid and Tailored Drilling Practices Help Successfully Drill and Complete Wells along the Minimum Horizontal Stress Direction in Khuff Reservoirs
Authors: Hegazy Mohamed , Sharma Sunil , Fares Khaled , Dave Alok
Published: October 2018

From a drilling operation's prospective, wells drilled in the direction of maximum horizontal stress are ideal as they have less risk of wellbore instability. Unfortunately, when these wells are hydraulically fractured, the fracture grows along the wellbore in the direction of well azimuth. To avoid overlapping of two adjacent induced fractures and thereby communication between stages, only two to three multistage fracture treatments can be performed.

To increase production and enhance recovery, operators now drill horizontal wells along the minimum horizontal stress direction to generate multiple transverse fractures during the stimulation stage. However, drilling along the minimum horizontal stress direction requires higher mud weights to minimize formation breakout due to prevailing in-situ stress conditions. This increased mud weight leads to higher differential pressures across the depleted reservoir layers, which when coupled with formation instability, creates greater challenges like stuck pipe, breakouts, and breakdowns. During the completion phase, the stiff multistage fracturing string containing multiple packers makes it more difficult to run through a slim hole, resulting in multiple failures, stuck incidents, and non-productive time.

While drilling high angle wells in Saudi Arabia's onshore gas fields, production holes are drilled through many reservoir layers. A few are highly depleted and the rest are highly pressurized. A high degree of skill is required to manage drilling fluids and drilling practices so that the depleted layers seal efficiently, thus preventing development of a thick filter cake and differential sticking incidents.

This paper outlines a successful neoteric methodology adopted in these challenging wells after a detailed analysis of offset data and failure incidents. The outstanding performance achieved by adaptation in operational procedures while using the existing setup in both drilling and completion phases, challenges while implementing the strategies, and comparison of results with previous drilling techniques will also be covered in detail to help operators drilling in similar conditions.
Testing and Evaluation of Shale Stability for Zubair Shale Formation
Authors: Abbas Ahmed K. , Flori Ralph E. , AL-Anssari Ahmed , Alsaba Mortadha
Published: October 2018

Zubair Formation consists of approximately 55% shale, which causes almost 70% of wellbore problems due to incompatibilities of drilling fluids and shale formations. The most common and effective solution to shale instability is through the design and selection of drilling fluids. Understanding the interaction between drilling fluids and shale has been a challenge due to the complexity of both physical and chemical variations in shale formations. This paper presents some of the important laboratory and well-site testing techniques that are often used by mud engineers for characterizing and remediating drilling fluids and shale interaction. Well-preserved shale samples were analyzed to describe the special characterization of the Zubair shale. Moreover, the traditional laboratory methods such as capillary suction time test, hot rolling dispersion test, bulk hardness test, and the linear swelling test were used to evaluate the stability of shale in the presence of test fluids. Our laboratory test results show that the Zubair shale is composed mainly of brittle mineral (quartz and calcite) with average content 51.46% and 43.54% of the clay mineral. In addition, the cation exchange capacity analysis and capillary suction time test indicated that Zubair shale has low to moderate reactivity with drilling fluids. This paper will present the preliminary process of analysis and understanding of shale structural failures due to shale/fluid interactions. Consequently, it can be used to control and minimize shale instability by improving the selection of chemical additives for clay inhibition.
Rheology Studies of Crosslinked Fracturing Fluids Utilizing Seawater
Authors: Yamak Raafat M. , Nasr-El-Din Hisham A. , Rahim Sabiq , BinGhanim Ahmed
Published: October 2018

Seawater-based fracturing fluids are used to generate a stable crosslinked fracturing fluid for higher formation temperatures primarily using two types of guar derivatives—HPG and CMHPG. Results are compared to other seawater-based fracturing fluid data in the literature, as well as to traditionally used freshwater-based fracturing fluid. A HP/HT rotational rheometer for viscosity measurements was utilized to assess the stability of the fracturing fluid. Chlorous-acid-based breakers were also tested in the fracturing fluid at various temperatures to assess effectiveness of reducing fluid viscosity. Additionally, a phosphonate-based scale inhibitor was used at various concentrations to mitigate the formation of scales that arise from the mixing of seawater and formation water. The concentration of the additives and consequent results are all provided to display the functionality of the crosslinked gel using seawater.

Results confirmed that a dual-crosslinked seawater-based fracturing fluid stability was feasible, as viscosity remained greater than 500 cP for two hours. Freshwater-based fracturing fluid displayed longer gel stability primarily because of a lack of salts that hinder polymer hydration. The stability time varied owing to the thermal degradation of the polymer. The high total of dissolved solids of the seawater had minimal effect on the breaker, as it reduced the stability time of the seawater-based fracturing fluids by over 50%. The introduction of new, cost-effective fracturing fluid formulations introduces environmentally friendly ways to find substitutes for fresh water in hydraulic fracturing operations.
Shale-Fluid Sensitivity for an Unconventional Shale Formation
Authors: Alouhali Raed , Al-Arfaj Mohammed , Al-Rashead Mohammed , Hayton Shaun
Published: October 2018

Shale instability is a major problem for drilling operation. Shales make up over 75% of drilled formations and are responsible for 90% of wellbore instability problems. There are many problem associated with shale drilling such as wellbore instability, cuttings dispersion, stuck pipe, and bit balling. Shales used to be the problematic formations that needed to drill through with the minimum number of problems, lowest possible lost time and generally the shortest possible open-hole time to minimize fluid sensitivity issues. However, with the advancement in drilling and production techniques, some shale formations are now the target for hydrocarbon production, meaning that much longer periods of open-hole are required for horizontal drilling, logging and completion meaning fluid sensitivity is an increasingly important question..

In this paper, we evaluate the shale-fluid sensitivity of a target unconventional shale formation. A total of 20 samples from 6 different wells were used in this study. The mineralogy of all 20 samples were analyzed using XRD. Then two shale-fluid sensitivity tests (dispersion test and capillary suction time test) were run on the samples using four different water based fluids (7% KCl, 3% KCl, 3% NaCl, and 5% NH4Cl) in addition to fresh water and diesel which were used to establish bounding parameters.

Overall, all inhibitors performed well, with 7% KCl and 3% KCl being the best performing inhibitors for drilling shale formations of this composition followed by 5% NH4Cl and lastly 3% NaCl.
Adding More Value in the Downturn Time from Digital Oil Field; what is more to leverage?
Authors: Al Qahtani Abdullah M. , Al Qahtani Mubarak
Published: October 2018

Operators have gotten onboard on digital field implementations as a game-changer to optimize oil production, reduce asset development and operation cost by leveraging fields' real-time data, software solutions, and smart workflow processes. Such implementations have demonstrated improved well placement, well delivery, real-time well monitoring, reduced production loss from unplanned well downtime, and improved HSE.

This paper covers overlooked aspects of digital oil field applications and focus on developing ADHOC solutions pertaining to well/reservoir performance optimization. These solutions will help identify high value opportunities and candidate wells for intervention and workover using operating envelopes, developing robust systems for well integrity and equipment reliability. The solutions will provide transient flow/pressure analyses for reservoir and well performance optimization and provide assessment of well productivity and well system performance. Moreover, the introduced solutions will enable efficient reservoir management and performance analytic tools, well flow capacity validation and optimization (single well/multiple wells). It will also enable horizontal/multi-lateral well monitoring and optimization.

New indices/metrics are introduced to gage the performance and ensure maximum return possible, well architected/subsystem designs, reliability, integrity, and well control issues. These indices are meant to develop life-cycle designs and assure total well management. In addition, to promote operational excellence and improve reliability.
A Fast Algorithm for the Estimation of the Equivalent Permeability in Heterogeneous Formations
Authors: Liao Qinzhuo
Published: October 2018

Reservoir simulation is an important component of reservoir development and management. Due to the heterogeneity in the subsurface formation, the accurate representation of the reservoir requires high-resolution geostatistical modeling with extremely large numbers of grid blocks in realistic models, which can be computationally prohibitive. This motivates the development of upscaling methods from fine-scale to coarse-scale by estimating the equivalent permeability. In this work, we have developed a rapid analytical method to increase the absolute permeability of heterogeneous reservoirs.

The equivalent permeability for a uniform flow in a given direction is bounded by (1) the harmonic mean of the arithmetic means of the local permeabilities, calculated over each slice of cells perpendicular to the given direction (upper bound), and (2) the arithmetic mean of the harmonic means of the local permeabilities, calculated on each line of cells parallel to the given direction (lower bound). The idea is to take a value between these two bounds, and the weighting coefficient is the key for accurate results. We presented a fast algorithm to estimate the weighting coefficient in the sense of probability expectation.

We compared the pressures and velocities calculated from three approaches, the fine-scale model, the coarse-scale model by numerical upscaling, and the coarse-scale model by analytical upscaling. We considered various conditions, including uncorrelated and correlated, isotropic and anisotropic, the effects of permeability variance and grid block geometry. We found that the pressures and velocities calculated from the coarse-scale model by analytical upscaling are very close to those from the coarse-scale model by numerical upscaling, i.e., the analytical method is as accurate as the numerical method, while the former can be O(10) times faster than the latter.
Unleashing the Limitation of Extended-Reach Wells; a Flow Optimization Well Architecture
Authors: Al Qahtani Abdullah M.
Published: October 2018

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

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

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

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

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

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

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

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

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

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

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.
Intelligent Prediction of Acid-fracturing Performance in Carbonates Reservoirs
Authors: Eliebid Mohammed , Hassan Amjed M. , Mahmoud Mohamed , Abdulraheem Abdulazeez , Elkatatny Salaheldin
Published: October 2018

Acidizing joint hydraulic fracturing is a widely used technique to increase the fracture conductivity in carbonate reservoirs stimulation. Predicting acid fracturing performance for optimum fracturing job design requires a detailed understanding of acid-rock reactions, rock strength and the stress applied to the rock and their effect on the fracture conductivity. The available models have many prediction limitations to acidized fracture conductivity with closure stress. Artificial intelligence is suggested to obtain a more precise prediction for acid-fracture conductivity. Artificial neural network (ANN) and adaptive network- based fuzzy inference system (ANFIS) are used to develop intelligent models capable of delivering an accurate design to acid-fracture conductivity. Published experimental data from the literature is used to train the models. 70% of the data was used for training and 30% was used in testing. The results showed that both ANN and ANFIS models outperformed the currently available models. ANFIS subtractive clustering with 0.4 cluster radius showed the best match to experimental data with 1.36% average percentage error and 0.998 R2.
Cement Evaluation Challenges
Authors: Gowida Ahmed H. , Ahmad Zainaddin , Elkatatny Salaheldin , Mahmoud Mohamed
Published: October 2018

Cement Evaluation Logs are generally used to evaluate the quality of the cement sheath behind the casing or liner before a well test or production operation is performed in the well. In many countries, regulatory authorities require that cement evaluation logs be done in every well after cementing operations to investigate the presence and quality of the cement sheath. The main objective of the cement evaluation process is to evaluate the hydraulic isolation of the cement sheath. The main principle of cement bond log (CBL) tools is to send an acoustic signal inside the casing and measuring the acoustic impedance of the cement between the casing and the formation. The interpretation and processing of these measurements are sometimes complicated and difficult to understand because CBL can be affected by the downhole conditions and the input parameters for processing. Therefore, it is important to understand how the log is processed before planning for any remedial action based on the apparently poor cement quality obtained from the log. Also, there are some challenges such as lightweight cement, the presence of micro-annulus, and eccentricity of the tool itself may affect the measurement and the relating remedial action to solve the problem.

Conventional cement evaluation tools are useful in most scenarios, but in more challenging operations, there is a pressing need for more advanced cement evaluation techniques to increase the confidence of the measurements for the evaluation of wellbore integrity. This paper demonstrates ultrasonic cement evaluation tools principles and examples for evaluating the quality of the cement bond. Also, the most common challenges and conditions which affect the sonic signal and lead to falsely indication of effective cement integrity. The processed case studies highlight the confidence of the cement isolation identified using these tools and evaluation techniques and their impact on the upcoming remedy actions.
Feasibility Study of 3000m Horizontal Section of Jiaoye 2-5HF Horizontal Extended-reach Well in Fuling Shale Gas, China
Authors: Li Xin , Gao Deli , Hu Degao , Zheng Youheng , Liao Rugang
Published: October 2018

The accurate prediction and improvement of the maximum measured depth (MMD) of horizontal extended-reach well (ERW) play a major role in improving the economic benefits and avoiding drilling risks of horizontal ERW in Fuling shale gas, China. In this study, a model to predict the MMD of horizontal ERW is presented, and the MMD of Jiaoye 2-5HF well in Fuling Shale Gas is determined. The MMD prediction model is established based on the dynamic pressure balance of bottom hole and the restriction of drilling pump. Considering the bearing capacities of drilled formation and drilling pump, this paper predicts the MMD, especially the horizontal-section limit of Jiaoye 2-5HF well in Fuling shale gas based on the extended-reach limit theory, determining whether it can meet the drilling need of 3000m horizontal section. The results show that the well's MMD is 6928m, and the horizontal- section limit is 3985m according to the casing programme and drilling parameter of Jiaoye 2-5HF, which can meet the drilling need of 3000m horizontal section. The MMD first increases and then decreases with the increase in drilling fluid flow rate. In addition, the Jiaoye 2-5HF well cannot meet the drilling need of 3000m horizontal section when the drilling fluid flow rate is greater than 38L/s or the rated pressure of drilling pump is less than 23.5MPa. Furthermore, the constraint conditions for the extension ability of horizontal ERW under different drilling parameters are not the same, and the MMD values are also different. Therefore, it is necessary to calculate separately. This work provides a practical tool for predicting the Jiaoye 2-5HF horizontal well's MMD in Fuling shale gas. The study is significant to avoid drilling hazards, maximize reservoir contact and obtain good economic benefits of Fuling shale gas in China.
Measure the Effectiveness of Frac Sleeves Integrity in Multi-Stage Completions using Multi-Finger Caliper and Acoustic Flow-Analyzer
Authors: Saada Mahmoud , Ismail Moustafa , Lu Yanjie , Zhang Wenting , Rourke Marvin , AbdelMoula Mongi
Published: October 2018

With the increased demand of Hydrocarbons, the industry trend to continually develop the unconventional reserves and/or maximize the HC production; different wellbores are accordingly drilled and completed with multi-stage completions. Frac Sleeve in Multi Stage completion tend to show several integrity issues. Multi-finger caliper tool has a proven capability of assessing the wellbore obstructions and internal corrosive damage to tubulars.

Gathering appropriate logging data is important for monitoring well-head and annuli pressures. Evaluating the Frac sleeves integrity whether are closed or open as well as to look for deformation within the sleeves are of client's objectives to confirm its conditions. We will combine the caliper data with passive noise detection tools to confirm and understands the fluid flow behavior in the well-bore and behind casing. Same combination is used to assess the injection sweep efficiency and anisotropy.

Caliper 3D visualization and advanced noise detection processing software were used to quickly and precisely understand the condition of the Frac sleeves in several wells for the sake of injection assessment and for future remedial actions to the recommended sleeves. Also, through comprehensive analysis, caliper logs have been used to assess scale buildup, paraffin or other mineral deposits in the wellbore which could coat the perforations, casing, tubulars, valves, pumps and downhole equipment.

The proposed paper will summarize field results and data gathered from several passes across the Frac sleeves. Using the logging data, we will measure the effectiveness and integrity of the Frac Sleeve for the purpose of injection/production assessment.
A Numerical Analysis for Acid Bullheading Treatment in Oil and Gas Wells Using Lattice Boltzmann Method
Authors: Alruwaili Khalid M. , Noui-Mehidi Mohamed N.
Published: October 2018

Acid bullheading is a well-known treatment for removing downhole scale and formation damage in oil and gas fields. The treatment is performed by using a controlled percentage of hydrogen chloride, and is normally operated under restricted wellhead pressure for health, safety, and environment (HSE) and well integrity purposes. These constraints sometimes prevent the treatment to be more effective in removing formation damage and improving injectivity and/or productivity of oil and gas wells. The present work is related to a fundamental study of fluid invasion in different types of formation with regard to the designed acid bullheading setups. The results are compared and analyzed based on a numerical investigation for the same geometrical and dynamical parameters to assess the efficiency of the fluid invasion designed process. The new treatment design utilizes the concept of cyclic pressure by fluctuating the injection velocity. In this paper, a mathematical formulation of fluid flow invasion in a porous medium is presented using a lattice Boltzmann numerical analysis. This method uses a mesoscopic approach to solve the flow problem instead of using Navier-Stokes equations and giving good numerical accuracy. It mimics different formation porosities, injection velocities, and procedures over time. A qualitative comparison between constant and variable injection velocities was performed under a fixed period of time. The new simulation code visually demonstrates the growth of fluid invasion in the investigated domains and highlights clear differences between the two cases of continuous and cyclic injections. This approach presents a new design in matrix stimulation that could effectively optimize oil and gas treatment operations.
Well-Placement Optimization in a Novel Heavy Oil Recovery Process Using In-Situ Steam Generated by Thermochemicals
Authors: Moussa Tamer , Patil Shirish , Mahmoud Mohamed , Elkatatny Salaheldin
Published: October 2018

Determination of optimal well locations plays an important role in the efficient recovery of hydrocarbon resources. However, it is a challenging and complex task because it relies on reservoir, and fluid and economic variables that are often nonlinearly correlated. Traditionally, well placement optimization (WPO) has been done through experience and use of quality maps. However, reservoir management teams are beginning to appreciate the use of automatic optimization tools for well placement that will yield the largest financial returns or highest net present value (NPV). In addition, the performance of a reservoir is time and process dependent, therefore well placement decisions cannot be based on static properties alone. On the other hand, well placement optimization requires a large number of simulator runs in an iterative process, and thus several runs to reach the maximum achievable NPV. Therefore, there is a real need for automatic well placement approach that uses highly efficient optimization method, which can improve the result quality, speed of the convergence process to optimal result and thus decrease the time required for computation.

The objective of this work is to determine the optimal well locations in a heavy oil reservoir under production using a novel recovery process, in which steam is generated, in-situ, using thermochemical reactions. Self-adaptive differential evolution (SaDE) and particle swarm optimization (PSO) methods are used as the global optimizer to find the optimal configuration of wells that will yield the highest NPV. Comparison analysis between the two proposed optimization techniques is introduced. The CMG STARS Simulator is utilized in this research to simulate reservoir models with different well configurations.

Comparison of results is made between the NPV achieved by the well configuration proposed by the SaDE and PSO methods. The results show that SaDE performed better than PSO in terms of higher NPV after ten years of production while under in-situ steam injection process using thermochemical reactions. This is the first known application where SaDE and PSO methods are used to optimize well locations in a heavy oil reservoir that is recovered by injecting steam generated in-situ using thermo-chemical reactions. This research shows the importance of well placement optimization in a highly promising and novel heavy oil recovery process. This also is a step forward in the direction to eliminate the CO2 emissions related to thermal recovery processes.