bluuebunny/crossref_metadata_2025 huggingface dataset:
Slim Pulsed Neutron Spectroscopy Lands for the First Time in the Egyptian Western Desert to Provide a Complete Formation Evaluation – A Novel Case Study from Abu Sennan Field
Authors: Saied Hesham , Refaat Emad , Mokhtar Hesham , Farouk Mohamed , Salim Sadek , Saleh Khaled , Gomes Renata , Cavalleri Chiara
Published: October 2021

Wellbore stability issues associated with mechanical failure of the formations frequently present a challenging environment for running openhole logs. Alternatively, casedhole logs can be used to provide multiple physical properties of the formation to help in reservoir characterization. Generally, conventional casedhole measurements have limitations due to the effect of borehole fluids as well as cement integrity. Therefore, it can be challenging to complete an accurate full reservoir evaluation using conventional casedhole measurements. In a field example from the Western Desert, Egypt, a state-of-the-art advanced slim pulsed neutron technology was deployed for the first time to provide a comprehensive standalone formation evaluation without openhole data.

When hydrocarbon exploration and development move into more challenging environments, deployment of fit-for-purpose technologies is required. The advanced slim pulsed neutron processing algorithms were used for recording capture and inelastic elemental spectroscopy for rock elemental concentrations, including total organic carbon, detailed mineralogy, and matrix properties, simultaneously with sigma and other neutron-based outputs. By integrating the independent pulsed neutron measurements that are borehole self-compensated, casedhole reservoir characterization is now feasible with high accuracy and precision where the conveyance of openhole logging is of high risk due to borehole conditions and wellbore stability issues.

This case demonstrates the applicability of advanced slim pulsed neutron logging for comprehensive reservoir characterization in casedhole environments without any openhole data. It presents this innovative approach for the first time in the Egyptian Western Desert in a field with complex geological background, mixed lithology, and reservoir fluids types. The characterization paved the way to a new gas discovery in a complex clastic environment with a total net pay of 36m of gas condensate. Detailed mineralogy, reservoir porosity, fluid types identification, and hydrocarbon saturation were obtained with high accuracy behind the cased borehole independent of cement integrity and borehole fluid invasion. Leveraging this novel approach, the same methodology can be applied to old reservoirs with limited openhole data available to provide a robust formation evaluation that would aid in re-assessing brownfields.

The advanced slim pulsed neutron spectroscopy establishes a paradigm shift in reservoir characterization for casedhole environments to provide a comprehensive formation evaluation and fluid saturation without any openhole input. The workflow can be implemented in various scenarios as a cost-effective solution for reservoir evaluation or reservoir management applications.
Numerical Investigation of Wettability Effects on Two-Phase Flow in Naturally Fractured Reservoirs Using Complex System Modelling Platform
Authors: Sedaghat Mohammad , Dashti Hossein
Published: October 2021

Wettability is an essential component of reservoir characterization and plays a crucial role in understanding the dominant mechanisms in enhancing recovery from oil reservoirs. Wettability affects oil recovery by changing (drainage and imbibition) capillary pressure and relative permeability curves. This paper aims to investigate the role of wettability in matrix-fracture fluid transfer and oil recovery in naturally fractured reservoirs. Two experimental micromodels and one geological outcrop model were selected for this study. Three relative permeability and capillary pressure curves were assigned to study the role of matrix wettability. Linear relative permeability curves were given to the fractures. A complex system modelling platform (CSMP++) has been used to simulate water and polymer flooding in different wettability conditions. Comparing the micromodel data, CSMP++ and Eclipse validated and verified CSMP++. Based on the results, the effect of wettability alteration during water flooding is stronger than in polymer flooding. In addition, higher matrix-to-fracture permeability ratio makes wettability alteration more effective. The results of this study revealed that although an increase in flow rate decreases oil recovery in water-wet medium, it is independent of flow rate in the oil-wet system. Visualized data indicated that displacement mechanisms are different in oil-wet, mixed-wet and water-wet media. Earlier fracture breakthrough, later matrix breakthrough and generation and swelling of displacing phase at locations with high horizontal permeability contrast are the most important features of enhanced oil recovery in naturally fractured oil-wet rocks.
Investigation of Well Control Parameterization with Reduced Number of Variables Under Reservoir Uncertainties
Authors: Santos Daniel Rodrigues , Fioravanti André Ricardo , Santos Antonio Alberto Souza , Schiozer Denis José
Published: October 2021

Although several studies have shown that life-cycle well control strategies can significantly improve a field's economic return, the industry often relies on short-term strategies. One drawback of traditional parameterization, adopted for well control life-cycle numerical optimization, is that it often generates control strategies that yield impractical abrupt changes in production curves. Another issue, especially in cases with a large number of decision variables, is the local optima convergence related to the non-convex optimization problems. In this context, we proposed and compared four life-cycle well control parameterizations to maximize the net present value (NPV) of the field under uncertainties, which are able to mitigate both the above-mentioned problems.

The first parameterization optimizes the apportionment of well rates at the beginning of the field management and well shut-in time. The other three are based on optimizing the coefficients of parametric equations (first-and second-order polynomials, and logistic equation) to guide the bottom-hole pressure (BHP) over time. We executed each parameterization five times in a deterministic reservoir scenario and compared them with well control short-term strategy that prioritizes production in wells with higher oil-water ratio and that aimed to replicate the general industry practice. In this strategy, the wells’ priority rank was updated at every 30-simulation days. Subsequently, the best parameterization was used to select the well control life-cycle strategy under reservoir uncertainties and this strategy was applied to the reference model representing a real reservoir.

The results showed that all the proposed parametrizations significantly improved the NPV in comparison to the well control short-term strategy, while simultaneously ensuring a smooth well production curve. The logistic equation presented the best result among all parameterizations, as it delivered both the highest average of NPV and the smallest dispersion over the five experiment repetitions. This parameterization also produced similar results when applied under uncertainties and for the reference model. These results endorse the importance of not only relying on a short-term strategy, but also planning it for the life-cycle.
Horizontal Versus Vertical Wells: Assessment of Sweep Efficiency in a Multi-Layered Reservoir Based on Consecutive Inter-Well Tracer Tests - A Comparison Between Water Injection and Polymer EOR
Authors: Davidescu Bogdan-George , Bayerl Mathias , Puls Christoph , Clemens Torsten
Published: October 2021

Enhanced Oil Recovery pilot testing aims at reducing uncertainty ranges for parameters and determining operating conditions which improve the economics of full-field deployment. In the 8.TH and 9.TH reservoirs of the Matzen field, different well configurations were tested, vertical versus horizontal injection and production wells. The use of vertical or horizontal wells depends on costs and reservoir performance which is challenging to assess. Water cut, polymer back-production and pressures are used to understand reservoir behaviour and incremental oil production, however, these data do not reveal insights about changes in reservoir connectivity owing to polymer injection. Here, we used consecutive tracer tests prior and during polymer injection as well as water composition to elucidate the impact of various well configurations on sweep efficiency improvements.

The results show that vertical well configuration for polymer injection and production leads to substantial acceleration along flow paths but less swept volume. Polymer injection does not only change the flow paths as can be seen from the different allocation factors before and after polymer injection but also the connected flow paths as indicated by a change in the skewness of the breakthrough tracer curves. For horizontal wells, the data shows that in addition to acceleration, the connected pore volume after polymer injection is substantially increased. This indicates that the sweep efficiency is improved for horizontal well configurations after polymer injection.

The methodology leads to a quantitative assessment of the reservoir effects using different well configurations. These effects depend on the reservoir architecture impacting the changes in sweep efficiency by polymer injection. Consecutive tracer tests are an important source of information to determine which well configuration to be used in full-field implementation of polymer Enhanced Oil Recovery.
Advances in Cased-Hole Formation Evaluation—The Access to Untapped Tight-Gas Resources in Mature Fields: A Case Study from the Dnieper-Donets Basin, Ukraine
Authors: Zambrano Rafael , Makar Yevhen , Sadivnyk Michael , Butenko Andriy , Doroshenko Oleksandr , Novikov Volodymyr , Cimic Miljenko , Cavalleri Chiara , Ahmad Samira , Akashev Yernur
Published: October 2021

The Sakhalin Field is located in the Dnieper-Donets Basin, east of Ukraine, and has been producing 7.7 billion cubic meters of natural gas in place from carboniferous rocks since the 1980s. Notwithstanding, it is strongly believed that significant untapped resources remain in the field, specifically those classified as tight intervals. Advances in wireline logging technology have brought, besides better accuracy on measurements behind the casing, a new measurement called fast neutron cross-section (FNXS), which has proved to be sensitive enough to the volume of gas in low-porosity formations. This enabled a quantitative interpretation for a better understanding of where these additional resources may lie in the Sakhalin Field.

The methodology is based on advanced pulsed neutron spectroscopy logs to assess the essential formation properties such as lithology, porosity, and gas saturation and reduce the evaluation uncertainty in potential tight gas intervals. The advanced technology combines measurements from multiple detectors that represent independent formation properties such as formation sigma, thermal neutron porosity, FNXS, and elemental fractions. To address the lithology, the tool measures directly the rock elements required to determine representative mineralogy and matrix properties, which in turn are used to compensate for the matrix effects and obtain a reliable porosity and gas volume estimation.

The methodology was tested on the upper Visean productive zones (Mississippian epoch) characterized by its low porosity (<10 pu) and permeability (<10 mD). In the past, those intervals have been overlooked because of inconclusive petrophysical interpretation based on basic openhole logs and their low production in some areas of the field. The necessity to finding new reserves has motivated the re-evaluation of possible bypassed tight-gas intervals by logging of mature wells behind casing in different sectors of the field. Advanced pulsed neutron spectroscopy logging behind casing uniquely identifies reserves in tight-gas intervals where basic open-hole interpretations were ambiguous. The gas production obtained from the perforated intervals supports the formation evaluation parameters estimated from the standalone interpretation of the pulsed neutron data.

This work describes in detail the application of the alternative methodology and interpretation workflow to evaluate the formation through the casing. A concrete example is presented to illustrate the effectiveness of this approach in the revealing and development of tight gas reservoirs in mature fields in the Dnieper-Donets Basin.
An Integrated Modeling Approach to Estimate the Shut-in Wellhead Pressure for Well Integrity Applications – Case Study
Authors: Mohamed Mohamed Ibrahim , El-Menoufi Ahmed Mahmoud , Ezz El-Regal Eman Abed , Hegazy Abd Allah Moustafa , Mansour Khaled Mohamed , Negm Mohamed Nagy , Hussein Hatem Mohamed
Published: October 2021

Oil and gas operators must measure or calculate the shut-in wellhead pressure for well integrity applications. Some operators adopt a method that gives satisfactory results for dry gas and lean gas condensate. They are using the steady-state simulator to calculate the wellhead pressure at a very low gas rate. The friction losses become negligible, and the only losses are due to hydrostatic head simulating (to some extent) the shut-in condition. This method again can work well with oil producers with low GOR/Bubble point pressure as the production string will be nearly a single phase. The problem is that this method is inaccurate for high GOR/CGR wells because of phase redistribution and the error can be significantly high.

Phase redistribution occurs After shut-in, liquid droplets will accumulate at the bottom of the well. The interface liquid/gas will move up with sometimes liquid cushion is being re-injected back in the reservoir due to gravity or gas expansion in the tubing while the gas/liquid interface will move down a little. Many factors affect the behavior, including the well deviation, fluid properties, and the productivity and the injectivity of the formation. Thus, simulating this behavior requires a dynamic multiphase simulation. As some of the fluids might return to the formation, as a result of compressibility, coupling with a numerical reservoir simulation to model the near wellbore is also necessary.

In this paper, we applied a dynamic multiphase model to predict the shut-in wellhead pressure. We used an uncertainty analysis approach to investigate the effect of many parameters on the accuracy of the results. We presented all recommended calculation procedures with a guide to minimizing the uncertainty associated. We presented our approach to three actual wells with different configurations and fluid properties with a deviation of +-10% of the real measurements.
Real-Time EM Look-Ahead Resistivity for Mapping of Oil/Water Contact While Drilling at Low Deviated Well, New Perspective for Mature Oil Fields Development: A Case Study Of Umm Gudair Field, Kuwait
Authors: Al-Khalifa Nasser Faisal , Hassan Mohammed Farouk , Joshi Deepak , Tiwary Asheshwar , Pasaribu Ihsan Taufik , Siam Mahmoud , Leveque Soaziq , Osman Salih Noreldin
Published: October 2021

The Umm Gudair (UG) Field is a carbonate reservoir of West Kuwait with more than 57 years of production history. The average water cut of the field reached closed to 60 percent due to a long history of production and regulating drawdown in a different part of the field, consequentially undulating the current oil/water contact (COWC). As a result, there is high uncertainty of the current oil/water contact (COWC) that impacts the drilling strategy in the field. The typical approach used to develop the field in the lower part of carbonate is to drill deviated wells to original oil/water contact (OOWC) to know the saturation profile and later cement back up to above the high-water saturation zone and then perforate with standoff. This method has not shown encouraging results, and a high water cut presence remains.

An innovative solution is required with a technology that can give a proactive approach while drilling to indicate approaching current oil/water contact and geo-stop drilling to give optimal standoff between the bit and the detected water contact (COWC). Recent development of electromagnetic (EM) look-ahead resistivity technology was considered and first implemented in the Umm Gudair (UG) Field. It is an electromagnetic-based signal that can detect the resistivity features ahead of the bit while drilling and enables proactive decisions to reduce drilling and geological or reservoir risks related to the well placement challenges.
Data-Driven PHM Solution for Health Monitoring of Mud Motor Power Sections While Drilling
Authors: Belov Dmitry , BA Samba , Liu Ji Tang , Kolyshkin Anton , Rocchio Sergio Daniel
Published: October 2021

Mud motors are widely used for directional and performance drilling. Due to the extremely challenging operating conditions, they are prone to failures, resulting in unnecessary maintenance repair costs as well as unpredictable and very costly drilling failure.

Until now, the oil and gas industry has lacked reliable procedures to monitor and maintain the health of the mud motor power sections. Recently, we systematically addressed this problem with an industry unique prognostic health management solution, which not only tracks remaining useful life (RUL), but also creates a new failure prevention scheme for operators. The key objective of this solution is to reduce maintenance costs and improve mud motor fleet reliability. It's based on a high-fidelity model and uses a hybrid approach by combining a high-fidelity physics-based model of a power section and data-driven approaches with machine learning techniques for real-time applications.

The new methodology was tested in the field with great success. The verification of the created solution was completed based on numerous field data from Saudi Arabia and Argentina. Comparison of the predicted mud motor fatigue values with the actual observed post-job conditions and job failures demonstrated high fidelity of the developed models. The whole solution is currently being integrated into a drilling platform including the maintenance system, the well construction planning, and the execution. The first application of the workflow was deployed in the field in Colombia targeting reduction of maintenance cost and failure avoidance. The result was outstanding, with the initial deployment bringing about 27% of projected yearly maintenance savings and 10% of projected yearly failure reduction. It enables using the equipment to the full extent with increased drilling performance without sacrificing reliability. In addition, it optimizes the entire fleet management with reduced cost of logistics and maintenance.

The findings of this paper demonstrate the value of the mud motor PHM solution for the oil and gas industry by providing accurate prognosis of power section health, leading to reduced costs, minimized NPT, and increased operational reliability.
Geomechanically Driven Casing Deformation During Multistage Perf and Plug Fracturing Operations: Investigation and Mitigation
Authors: Gandomkar Arjang , Katz David , Gomez Ricardo , Gundersen Anders
Published: October 2021

Casing Deformation has presented itself in numerous unconventional basins. Severe deformation interferes with multistage fracturing, in particular with plug-and-perforation (also known as plug-and-perf) operations, the most common stage isolation method in unconventional development. Casing Deformation can greatly impact 20-30% of field productivity of horizontal wells in certain US shale and tight oil fields (Jacobs, 2020). Reservoir accessibility and well integrity are the two separate issues when considering casing deformation. In this paper, the impact of geomechanically driven casing deformation on reservoir accessibility that in turn affects production and economics, will be discussed.

Origin of casing deformation within a target zone lies in natural fractures placed in highly anisotropic stress regimes. When these fractures are perturbed by hydraulic stimulation, slow slip or dynamic failure of the rock may occur. This phenomenon is intensified by active tectonics, high anisotropic in-situ stresses, and poor completion practices, i.e., poor cement. This paper evaluates these processes by demonstrating failure conditions of wellbores in different stress states and well orientations representative of unconventional basins. It reviews how these conditions can be evaluated in the reservoir, so risk can be estimated. The mitigation procedures to reduce casing deformation impact to operations through either well planning or completions design are discussed. Finally, this paper will also review an alternative completion method to plug-and-perf that allows limited entry completion technique in restricted ID casing due to casing deformation with a field case study.
New LWD Image Improvement Method to Mitigate Interpretation Risks
Authors: Zhang Tianhua , Yang Shiduo , Shrivastava Chandramani , A Adrian , Bize-Forest Nadege
Published: October 2021

With the advancement of LWD (Logging While Drilling) hardware and acquisition, the imaging technology becomes not only an indispensable part of the drilling tool string, but also the image resolution increases to map layers and heterogeneity features down to less than 5mm scale. This shortens the geological interpretation turn-around time from wireline logging time (hours to days after drilling) to semi-real time (drilling time or hours after drilling).

At the same time, drilling motion is complex. The depth tracking is on the surface referenced to the surface block movement. The imaging sensor located downhole can be thousands of feet away from the surface. Mechanical torque and drag, wellbore friction, wellbore temperature and weight on bit can make the downhole sensor movement motion not synchronized with surface pipe depth. This will cause time- depth conversion step generate image artifacts that either stop real-time interpretation of geological features or mis-interpret features on high resolution images.

In this paper, we present several LWD images featuring distortion mechanism during the drilling process using synthetic data. We investigated how heave, depth reset and downhole sensor stick/slip caused image distortions. We provide solutions based on downhole sensor pseudo velocity computation to minimize the image distortion. The best practice in using Savitsky-Golay filter are presented in the discussion sections.

Finally, some high-resolution LWD images distorted with drilling-related artifacts and processed ones are shown to demonstrate the importance of image post-processing. With the proper processed images, we can minimize interpretation risks and make drilling decisions with more confidence.
Numerical Analysis of the Behavior of Gas Hydrate Layers After Cementing Operations
Authors: Merey Sukru , Eren Tuna , Polat Can
Published: October 2021

Since the 2000s, the number of gas hydrate wells (i.e., exploration wells, production test wells) has increased. Moreover, in the marine environment, gas hydrate zones are drilled in conventional hydrocarbon wells. Different than conventional hydrocarbon wells, the heat released with cement hydration cannot be ignored because gas hydrates are heat sensitive. In this study, by analyzing different cement compositions (conventional cement compositions and novel low-heat of hydration cement), it is aimed to investigate the effect of the heat of cement hydration on gas hydrate zones near the wellbore. For this purpose, numerical simulations with TOUGH+HYDRATE simulator were conducted in the conditions of the Nankai Trough gas hydrates. According to the numerical simulations in this study, if the increase in temperature in the cemented layer is above 30°C, significant gas hydrate dissociation occurs, and free gas evolved in the porous media. This might cause gas channeling and poor cement bond. The heat released with cement hydration generally affects the interval between the cemented layer and 0.25 m away from the cemented layer. Within a few days after cementing, pressure, temperature, gas hydrate saturation, and gas saturation returned to almost their original values.
Effects of Non-Newtonian Fluid Characteristics on Flow Dynamics in Polymer Flooding: a Lattice Boltzmann Study
Authors: Wei Bei , Hou Jian , Zhao Ermeng
Published: October 2021

The flow dynamics of non-Newtonian fluid in porous media is much different from the Newtonian fluid. In this work, we establish a lattice Boltzmann model for polymer flooding taking into both the power law fluid properties and viscoelastic fluid properties. Using this model, we investigate the viscosity distribution in porous media, the local apparent permeability in porous media, and the effect of elastic force on the remaining oil in dead ends.

Firstly, we build a single phase lattice Boltzmann model to evolve the fluid velocity field. Then the viscosity and shear rate in each lattice can be calculated based on the relaxation time and velocity field. We further make the fluid viscosity change with the shear rate according to the power-law fluid constitutive equation, consequently establish the lattice Boltzmann model for power law fluid. Moreover, we derive the Maxwell viscoelastic fluid model in integral form using Boltzmann superposition principle, and the elastic force is calculated from the divergence of the stress tensor. We then couple the elastic force into the lattice Boltzmann model by Newton's second law, and finally establish the lattice Boltzmann model of the viscoelastic fluid. Both the models are validated against analytical solutions.

The simulation results show that when the power-law index is smaller than 1, the fluid viscosity shows a distribution of that viscosity is higher in pore center and lower near the wall; while when the index is larger than 1, the fluid viscosity shows a opposite distribution. This is because the pore center has a high velocity but a low shear rate, while the boundary has a low velocity but a high shear rate. Moreover, the local apparent permeability decreases with the power law index, and the number of hyper-permeable bands also decreases. In addition, the local permeability shows pressure gradient dependence. Considering the viscoelasticity effects, the displacement fluid has a clear tendency to sweep deeply into the dead end, which improves the oil washing efficiency of the dead end.

The model provides a pore scale simulation tool for polymer flooding and help understand the flow mechanisms and enhanced oil recovery mechanisms during polymer flooding.
Sharpening a Cyclic Solvent Injection Technique in a Heavy Oil Reservoir from the Variation of the Nitrogen Injected as Dispersing: A Field Pilot, Colombia
Authors: Arias Andres Solano , Navarro Edgar Garzon , Munevar Fernando Contreras , Ortiz Isaac Luque
Published: October 2021

This paper analyzes the use of a cyclic solvent injection technique (CSI) as a non-thermal EOR alternative to cyclic steam stimulation (CSS) for increasing the heavy oil recovery in a shallow reservoir located at the middle Magdalena basin in Colombia.

A pilot well with less than 30% of water-cut and 10.9 °API was selected. Heavy natural gasoline of 30 °API obtained from the same reservoir was injected by using nitrogen (N2) as dispersing gas.

Three procedures were performed being the procedure A, a Solvent slug injection of 60 bbl through the annular pushed and dispersed by 46,444 m3 (1,640 Mscf) of N2 immiscible (considering the low reservoir pressure). The procedure B consisted of injecting the same Solvent volume, but this time pushed by a third part of the N2 injected previously 15,481 m3 (547 Mscf). The procedure C consisted of only inject the same N2 volume than B procedure to analyze the heavy oil response without Solvent. There were collected production data, °API (by hydrometer), gas-flow and gas-gravity values using a liquid level software. Knowing the °API of each component in the laboratory—Solvent and heavy oil—the Solvent concentration from the real °API produced in production stages was calculated. All procedures had 48 hours of soaking, followed by a flowing process to tank to carefully release the excess of N2 before starting the production stage, avoiding gas lock issues.

Without considering the Solvent injected, incremental oil production in procedure A was 232 bbl, in procedure B was 120 bbl and for procedure C, incremental oil only reached 11 bbl. With the last result it was determined the N2 injection by itself as a production mechanism without the Solvent effect in the in-situ heavy oil had a negligible effect on incremental oil. The gas-gravity showed the gas composition became heavier along the time, this considering the high-frequency N2 injections swept the methane near the well, requiring more time to produce the N2 traces from the porous media. The excess of N2 as a heavy Solvent dispersing mechanism does not warrant a better dilution effect since as observed in A and B procedures, Solvent concentration in the early production stage never dropped below 35% (17 °API), regardless of the N2 volume injected in the first two days.

Finally, although A procedure had more incremental oil production (+93% than B), less N2 injected in B procedure was more efficient (+55% than A) regarding the incremental oil and N2 injected ratio (ONR).
Generation of a Complete Profile for Porosity Log While Drilling Complex Lithology by Employing the Artificial Intelligence
Authors: Al-Sabaa Ahmed , Gamal Hany , Elkatatny Salaheldin
Published: October 2021

The formation porosity of drilled rock is an important parameter that determines the formation storage capacity. The common industrial technique for rock porosity acquisition is through the downhole logging tool. Usually logging while drilling, or wireline porosity logging provides a complete porosity log for the section of interest, however, the operational constraints for the logging tool might preclude the logging job, in addition to the job cost. The objective of this study is to provide an intelligent prediction model to predict the porosity from the drilling parameters.

Artificial neural network (ANN) is a tool of artificial intelligence (AI) and it was employed in this study to build the porosity prediction model based on the drilling parameters as the weight on bit (WOB), drill string rotating-speed (RS), drilling torque (T), stand-pipe pressure (SPP), mud pumping rate (Q). The novel contribution of this study is to provide a rock porosity model for complex lithology formations using drilling parameters in real-time. The model was built using 2,700 data points from well (A) with 74:26 training to testing ratio. Many sensitivity analyses were performed to optimize the ANN model. The model was validated using unseen data set (1,000 data points) of Well (B), which is located in the same field and drilled across the same complex lithology.

The results showed the high performance for the model either for training and testing or validation processes. The overall accuracy for the model was determined in terms of correlation coefficient (R) and average absolute percentage error (AAPE). Overall, R was higher than 0.91 and AAPE was less than 6.1 % for the model building and validation.

Predicting the rock porosity while drilling in real-time will save the logging cost, and besides, will provide a guide for the formation storage capacity and interpretation analysis.
Real-Time Solution for Down Hole Torque Estimation and Drilling Optimization in High Deviated Wells Using Artificial Intelligence
Authors: Elzenary Mahmoud Nader
Published: October 2021

This project provides a new realistic solution for the accuracy of down hole torque measurements using the integration of the Artificial intelligence (AI) technology with the downhole challenges being faced while drilling deep and high deviated wells. The new estimates are based on surface measurements which have the major influence on the bit torque (downhole torque) values while drilling. Artificial intelligence technology and its related applications such as; artificial neural network (ANN), support vector machine (SVM) and adaptive neuro fuzzy interference system (ANFIS) will be utilized to predict and estimate accurate wellbore torque which will be applied effectively to prevent real time stuck pipe situation through a friendly user software which will maintain the downhole torque within the SAFE zone by controlling the unified surface drilling variables such as; weight on bit (WOB), Rate of Penetration (ROP) and Flow Rate.

This downhole torque model will be validated and verified through a real drilling scenario from a field in north of Africa. The field data includes weight on bit, surface torque, stand-pipe pressure, and rate of penetration were collected from the mentioned well which had experienced a costly stuck pipe situation. However, with the provided model the same encountered scenario will be avoided, due to the optimization of the real time drilling variables and hence, saving the well and evade a costly non-productive time.
Unlocking the Potential of Electrical Submersible Pumps: the Successful Testing and Deployment of a Real-Time Artificially Intelligent System, for Failure Prediction, Run Life Extension, and Production Optimization
Authors: Al Radhi Mohammed , Bermudez Fernando Angel , Al Madhoun Wael , Al Blooshi Khaled , Al Nahhas Noor Nasser , Shono Mohammed Nazeih
Published: October 2021

This paper is a summary of the collaborative work between ADNOC (Abu Dhabi National Oil Company) and nybl, a deep tech development company, and the results of applying nybl's proprietary "Science-Based Artificial Intelligence" to ADNOC Electrical Submersible Pump (ESP) wells in real-time applications. The paper demonstrates the potential benefits of the real-life application of Artificial Intelligence (AI) / Machine Learning (ML) in conjunction with traditional Petroleum Engineering concepts and algorithms to predict imminent and future failures, extend and monitor run life, and maximize the production of ESPs. This paper will highlight ADNOC's innovative approach to pilot new technology through successful deployment on 27 wells, spread onshore and offshore, in real-time, with prescriptive actions.
Successful Application of Machine Learning to Improve Dynamic Modeling and History Matching for Complex Gas-Condensate Reservoirs in Hai Thach Field, Nam Con Son Basin, Offshore Vietnam
Authors: Hoang Son K. , Tran Tung V. , Nguyen Tan N. , Truong Tu A. , Pham Duy H. , Tran Trung N. , Trinh Vinh X. , Ngo Anh T.
Published: October 2021

This study aims to apply machine learning (ML) to make history matching (HM) process easier, faster, more accurate, and more reliable by determining whether Local Grid Refinement (LGR) with transmissibility multiplier is needed to history match gas-condensate wells producing from geologically complex reservoirs and determining how LGR should be set up to successfully history match those production wells.

The main challenges for HM gas-condensate production from Hai Thach wells are large effect of condensate banking (condensate blockage), flow baffles by the sub-seismic fault network, complex reservoir distribution and connectivity, highly uncertain HIIP, and lack of PVT information for most reservoirs. In this study, ML was applied to analyze production data using synthetic samples generated by a very large number of compositional sector models so that the need for LGR could be identified before the HM process and the required LGR setup could also be determined. The proposed method helped provide better models in a much shorter time, and improved the efficiency and reliability of the dynamic modeling process.

500+ synthetic samples were generated using compositional sector models and divided into training and test sets. Supervised classification algorithms including logistic regression, Gaussian, Bernoulli, and multinomial Naïve Bayes, linear discriminant analysis, support vector machine, K-nearest neighbors, and Decision Tree as well as ANN were applied to the data sets to determine the need for using LGR in HM. The best algorithm was found to be the Decision Tree classifier, with 100% and 99% accuracy on the training and the test sets, respectively. The size of the LGR area could also be determined reasonably well at 89% and 87% accuracy on the training and the test sets, respectively. The range of the transmissibility multiplier could also be determined reasonably well at 97% and 91% accuracy on the training and the test sets, respectively. Moreover, the ML model was validated using actual production and HM data.

A new method of applying ML in dynamic modeling and HM of challenging gas-condensate wells in geologically complex reservoirs has been successfully applied to the high-pressure high-temperature Hai Thach field offshore Vietnam. The proposed method helped reduce many trial and error simulation runs and provide better and more reliable dynamic models.
Discriminating Shale Layers by Pseudo CGR Logs Created Using Artificial Intelligence
Authors: Aldajani Saud K. , Alotaibi Saud F. , Abdulraheem Abdulazeez
Published: October 2021

The discrimination of shale vs. non-shale layers significantly influences the quality of reservoir geological model. In this study, a novel approach was implemented to enhance the model by creating Pseudo Corrected Gamma Ray (CGR) logs using Artificial Intelligence methods to identify the thin shale beds within the reservoir.

The lithology of the carbonate reservoir understudy is mostly composed of dolomite and limestone rock with minor amounts of anhydrite and thin shale layers. The identification of shale layers is challenging because of the nature of such reservoirs. The high organic content of the shales and the presence of dolomites, particularly the floatstones and rudstones, can adversely affect the log quality and interpretation and may result in inaccurate log correlations, overestimating/ underestimating Original Oil In Place (OOIP) and reservoir net pays.

In such cases, Corrected Gamma Ray (CGR) curves are typically used to identify shale layers. The CGR curve response is due to the combination of thorium and potassium that is associated with the clay content. The difference between the total GR and the CGR is essentially the amount of uranium-associated organic matter. Because of the very limited number of CGR logs in this reservoir, Artificial Intelligence (AI) approach was used to identify shale volume across the entire reservoir.

Synthetic CGR curves were generated for the wells lacking CGR logs using AI methods. Resistivity, Density, Neutron and total GR logs were used as inputs while CGR was set as the target. Five wells that have CGR logs were used to train the model. The created pseudo logs were then used to identify shale layers and could also be used to correct effective porosity logs.

After statistical analysis of the data, two different Artificial Intelligence Techniques were tested to predict CGR logs; Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN).

A Sugeno-type FIS structure using subtractive clustering demonstrated the best prediction with correlation coefficient of 0.96 and mean absolute percentage error (MAPE) of 20%. The resulting synthetic CGR curves helped identify shale layers that do not extend over the entire reservoir area and ultimately correct the effective porosity logs in the reservoir model. Porosity was primarily obtained from the neutron-density logs which results in very high porosity measurements across the shale layers.

This study shows a new workflow to predict shale layers in Carbonate reservoirs. The created pseudo CGR logs would help predict shale and is an added-value data that could be incorporated into the Earth model.
Deep Learning Enabled Deblurring of Computed Tomography Images of Porous Media
Authors: Alsamadony Khalid Labib , Yildirim Ertugrul Umut , Glatz Guenther , bin Waheed Umair , M. Hanafy Sherif
Published: October 2021

Computed tomography (CT) is an important tool to characterize rock samples allowing quantification of physical properties in 3D and 4D. The accuracy of a property delineated from CT data is strongly correlated with the CT image quality. In general, high-quality, lower noise CT Images mandate greater exposure times. With increasing exposure time, however, more wear is put on the X-Ray tube and longer cooldown periods are required, inevitably limiting the temporal resolution of the particular phenomena under investigation.

In this work, we propose a deep convolutional neural network (DCNN) based approach to improve the quality of images collected during reduced exposure time scans. First, we convolve long exposure time images from medical CT scanner with a blur kernel to mimic the degradation caused because of reduced exposure time scanning. Subsequently, utilizing the high- and low-quality scan stacks, we train a DCNN. The trained network enables us to restore any low-quality scan for which high-quality reference is not available. Furthermore, we investigate several factors affecting the DCNN performance such as the number of training images, transfer learning strategies, and loss functions.

The results indicate that the number of training images is an important factor since the predictive capability of the DCNN improves as the number of training images increases. We illustrate, however, that the requirement for a large training dataset can be reduced by exploiting transfer learning. In addition, training the DCNN on mean squared error (MSE) as a loss function outperforms both mean absolute error (MAE) and Peak signal-to-noise ratio (PSNR) loss functions with respect to image quality metrics.

The presented approach enables the prediction of high-quality images from low exposure CT images. Consequently, this allows for continued scanning without the need for X-Ray tube to cool down, thereby maximizing the temporal resolution. This is of particular value for any core flood experiment seeking to capture the underlying dynamics.
Prediction of Gas Viscosity of Yemeni Gas Fields Using Machine Learning Techniques
Authors: Deumah Salman Sadeg , Yahya Wahib Ali , Al-khudafi Abbas Mohamed , Ba-Jaalah Khaled Saeed , Al-Absi Waleed Tawfeeq
Published: October 2021

Gas viscosity is an important physical property that controls and influences the flow of gas through porous media and pipe networks. An accurate gas viscosity model is essential for use with reservoir and process simulators. The objective of this study is to assess the predictability of gas viscosity of Yemeni gas fields using machine learning techniques.

Performance of some machine learning techniques in the prediction of gas viscosity investigated in this work. The techniques include K-nearest neighbors (KNN), Random Forest (RF), Multiple Linear Regression (MLR), and Decision Tree (DT). About 440 data points were collected from different Yemeni gas fields were used to develop the machine-learning model. The input data used in the training include pressure, temperature, gas density, specific gravity, gas formation volume factor, gas deviation factor, gas molecular weight, pseudo-reduced temperature and pressure, pseudo-critical temperature and pressure, and non-hydrocarbon gas components (N2, CO2, and H2S). Part of the data (75%) was used to train the developed models using the algorithms while another part of the data (25%) was used to predict the viscosity of gas for samples. Trained machine learning models were constructed using the Python programming language. The performance and accuracy of the machine learning models were tested and compared their results based on four different functional input datasets. The result of this study found that that the DT model predicted the gas viscosity with higher accuracy, and gave very good results better than other models based on input parameters of the dataset (A) and (B). This was evidenced by lower the Root mean square error (0.000832), lower mean absolute percent relative error (0.042%), and higher coefficient of determination (R2=0.9465).

The proposed approach in the present study provides an accurate and inexpensive model for estimating the viscosity of gases as a function of all input parameters of the dataset (A). Overall, the relative effects of these different input parameters have verified that the gas viscosity has the uppermost relevant to the gas density and specific gravity that have the highest percentage of 51%.
Creating Opportunities for Successful Artificial Intelligence Applications in the Upstream Petroleum Industry
Authors: Anifowose Fatai Adesina
Published: October 2021

The petroleum industry has continued to show more interest in the application of artificial intelligence (AI). Most professional gatherings now have sub-themes to highlight AI applications. Similarly, the number of publications featuring AI applications has increased. The industry is facing the challenge of scaling up the applications to practical and impactful levels. Most of the applications end up in technical publications and narrow proofs of concept. For the industry's digital transformation objective to be fully achieved, efforts are required to overcome the current limitations. This paper discusses possible causes of the prevailing challenges and prescribes a number of recommendations to overcome them. The recommendations include ways to handle data shortage and unavailability issues, and how AI projects can be designed to provide more impactful solutions, regenerate missing or incomplete logs, and provide alternative workflows to estimate certain reservoir properties. The results of three successful applications are presented to demonstrate the efficacy of the recommendations. The first application estimates a log of reservoir rock cementation factors from wireline data to overcome the limitation of the conventional approach of using a constant value. The second application used the machine learning methodology to regenerate missing logs possibly due to tool failure or bad hole conditions. The third application provides an alternative approach to estimate reservoir rock grain size to overcome the challenges of the conventional core description. Tips on how these applications can be integrated to create a bigger impact on exploration and production (E&P) workflows are shared. It is hoped that this paper will enrich the current AI implementation strategy and practice. It will also encourage increased synergy and collaborative integration of domain expertise and AI methods to make better impact and achieve the digital transformation of E&P business goals.
Delayed Ureteral Fistula After Laparoscopic Lateral Lymph Node Dissection for Rectal Cancer: A Case Report and Literature Review
Authors: yang ying , Deng Xiang-Bing , Wei Ming-Tian , Meng Wen-Jian , Wang Zi-Qiang
Published: October 2021

Background: Ureteral injury is the most common urological complication of pelvic surgery. Lateral lymph node dissection (LLND) is a technically demanding procedure by laparoscopy after neoadjuvant chemoradiotherapy (nCRT), which may increase the risk of ureteral injury because the ureter and hypogastric fascia must be dissected from the vesicohypogastric fascia to preserve them. Case presentation: Here, we present a case of delayed ureteral leakage occurring in a 63-year-old male patient who underwent laparoscopic abdominoperineal resection (APR) with right LLND after nCRT. Ureteral reimplantation (vesicoureteric anastomosis) was performed in this patient due to the failure of the placement of the double J stent. Conclusions: Our case shows that ureteral leakage may occur in the pelvic segment of the ureter in the patient who underwent laparoscopic LLND, especially received preoperative radiotherapy or chemoradiotherapy.