publications
Recent publications emerging from energy and quantum computing.
2025
- Linear Optics to Scalable Photonic Quantum ComputingDennis Delali Kwesi Wayo, Leonardo Goliatt, and Darvish GanjiarXiv preprint arXiv:2501.02513, 2025
Recent advancements in quantum photonics have driven significant progress in photonic quantum computing (PQC), addressing challenges in scalability, efficiency, and fault tolerance. Experimental efforts have focused on integrated photonic platforms utilizing materials such as silicon photonics and lithium niobate to enhance performance. Parameters like photon loss rates, coupling efficiencies, and fidelities have been pivotal, with state-of-the-art systems achieving coupling efficiencies above 90% and photon indistinguishability exceeding 99%. Quantum error correction schemes have reduced logical error rates to below , marking a step toward fault-tolerant PQC. Photon generation has also advanced with deterministic sources, such as quantum dots, achieving brightness levels exceeding photon pairs/s/mW and time-bin encoding enabling scalable entanglement. Heralded single-photon sources now exhibit purities above 99%, driven by innovations in fabrication techniques. High-efficiency photon detectors, such as superconducting nanowire single-photon detectors (SNSPDs), have demonstrated detection efficiencies exceeding 98%, dark count rates below 1 Hz, and timing jitters as low as 15 ps, ensuring precise photon counting and manipulation. Moreover, demonstrations of boson sampling with over 100 photons underscore the growing computational power of photonic systems, surpassing classical limits. The integration of machine learning has optimized photonic circuit design, while frequency multiplexing and time-bin encoding have increased system scalability. Together, these advances bridge the gap between theoretical potential and practical implementation, positioning PQC as a transformative technology for computing, communication, and quantum sensing.
@article{wayo2025linear, title = {Linear Optics to Scalable Photonic Quantum Computing}, author = {Wayo, Dennis Delali Kwesi and Goliatt, Leonardo and Ganji, Darvish}, journal = {arXiv preprint arXiv:2501.02513}, year = {2025}, dimensions = {false}, }
2024
- Exploring Quantum-Dot Engineered Solid-State Photon Upconversion in PbS: Yb\^{3+}, Er\^{3+} /CuBiO Using Density Functional Theory and Machine Learning Methods for Water SplittingDennis Delali Kwesi Wayo, Vladislav Kudryashov, Mirat Karibayev, and 5 more authorsarXiv preprint arXiv:2501.00573, 2024
This study presents a comprehensive numerical analysis of a quantum-dot-engineered heterostructure, PbS:Yb3+,Er3+/CuBiO, optimized for water splitting applications. Using density functional theory (DFT) coupled with machine learning, the study explores the electronic, optical, and catalytic properties of the material. The optimized PbS structure exhibited a direct bandgap of 1.191 eV, while co-doping with Yb and Er transitioned the material to a metallic state, enhancing charge carrier mobility and electron-hole separation. The final heterostructure displayed an indirect bandgap of 0.431 eV, favorable for visible-light absorption. Key findings include an internal electric field strength of 6.3 Debye, efficient charge transfer confirmed by Bader analysis, and strong optical absorption at 2.4 eV. Machine learning models, including DNN and LSTM, were employed to predict photon absorption rates, achieving mean squared errors as low as 0.0004. The synergistic effects of enhanced internal electric fields, optimized band structures, and strong photon absorption underscore the material’s potential for efficient hydrogen production. This study bridges advanced computational methods and machine learning, offering a framework for designing high-performance photocatalysts in renewable energy applications.
@article{wayo2024exploring, title = {Exploring Quantum-Dot Engineered Solid-State Photon Upconversion in PbS: $ Yb\^{}$\{$3+$\}$, Er\^{}$\{$3+$\}$ $/CuBiO Using Density Functional Theory and Machine Learning Methods for Water Splitting}, author = {Wayo, Dennis Delali Kwesi and Kudryashov, Vladislav and Karibayev, Mirat and Fynn, Gertrude Ellen and Rafikova, Khadichakhan and Saporetti, Camila Martins and Goliatt, Leonardo and Nuraje, Nurxat}, journal = {arXiv preprint arXiv:2501.00573}, year = {2024}, dimensions = {false}, } - AI and Quantum Computing in Binary Photocatalytic Hydrogen ProductionDennis Delali Kwesi Wayo, Leonardo Goliatt, and Darvish GanjiarXiv preprint arXiv:2501.00575, 2024
Photocatalytic water splitting has emerged as a sustainable pathway for hydrogen production, leveraging sunlight to drive chemical reactions. This review explores the integration of density functional theory (DFT) with machine learning (ML) to accelerate the discovery, optimization, and design of photocatalysts. DFT provides quantum-mechanical insights into electronic structures and reaction mechanisms, while ML algorithms enable high-throughput analysis of material properties, prediction of catalytic performance, and inverse design. This paper emphasizes advancements in binary photocatalytic systems, highlighting materials like , , and , as well as novel heterojunctions and co-catalysts that improve light absorption and charge separation efficiency. Key breakthroughs include the use of ML architectures such as random forests, support vector regression, and neural networks, trained on experimental and computational datasets to optimize band gaps, surface reactions, and hydrogen evolution rates. Emerging techniques like quantum machine learning (QML) and generative models (GANs, VAEs) demonstrate the potential to explore hypothetical materials and enhance computational efficiency. The review also highlights advanced light sources, such as tunable LEDs and solar simulators, for experimental validation of photocatalytic systems. Challenges related to data standardization, scalability, and interpretability are addressed, proposing collaborative frameworks and open-access repositories to democratize DFT-AI tools. By bridging experimental and computational methodologies, this synergistic approach offers transformative potential for achieving scalable, cost-effective hydrogen production, paving the way for sustainable energy solutions.
@article{wayo2024ai, title = {AI and Quantum Computing in Binary Photocatalytic Hydrogen Production}, author = {Wayo, Dennis Delali Kwesi and Goliatt, Leonardo and Ganji, Darvish}, journal = {arXiv preprint arXiv:2501.00575}, year = {2024}, dimensions = {false}, } - Laboratory study investigating the impact of different LCMs additives on drilling mud rheology and filtrationZainab Jawad Aleqabi, Ayad A Alhaleem A Alrazzaq, and Dennis Delali Kwesi WayoIraqi Journal of Chemical and Petroleum Engineering, 2024
Drilling operations in Basra’s oil fields, particularly targeting the Dammam, Hartha, and Shuaiba formations, are facing significant challenges related to lost circulation. This study investigates the effects of incorporating lost circulation materials (LCMs) into bentonite-based and polymer-based drilling muds. Experiments were carried out using a high-pressure high-temperature filter press to evaluate the rheological properties and filtration performance of the different mud systems prepared using bentonite and polymer mixed with various compositions of additives. The results showed that the incorporation of LCMs increased the plastic viscosity and yield point of the polymer mud by 25-30%, while the impact on the bentonite mud was less significant. Notably, the using of fine-sized LCMs influenced the rheological characteristics of the polymer mud system, resulting in a 35-40% increase in parameters as the LCM concentration was raised. In terms of filtration performance, the bentonite mud exhibited the highest total fluid loss, whereas the polymer mud showed the lowest. The adding of LCMs led to a 20-25% reduction in fluid loss for both mud systems, with fine-sized LCMs at higher concentrations proving most effective in the polymer mud. In conclusion, this study demonstrates the substantial influence that the type, size, and concentration of LCMs can have on the rheological and filtration properties of drilling muds. It is confirmed that the polymer mud system is particularly sensitive to these LCM parameters. Desalination elimination of 80.95% associated with a maximum power output of 420 mW/m3 in the system.
@article{aleqabi2024laboratory, title = {Laboratory study investigating the impact of different LCMs additives on drilling mud rheology and filtration}, author = {Aleqabi, Zainab Jawad and Alrazzaq, Ayad A Alhaleem A and Wayo, Dennis Delali Kwesi}, journal = {Iraqi Journal of Chemical and Petroleum Engineering}, volume = {25}, number = {4}, pages = {115--122}, year = {2024}, dimensions = {false}, } - Molecular Dynamic Prognosis for Ti-C10H16N2O8 Filter Cake DecompositionS Irawan, DDK Wayo, E Bayramov, and 2 more authorsIn SPE Annual Caspian Technical Conference, 2024
Chelating agents such as Ethylene-Diamine-Tetra-Acetic (EDTA) had been suggested to be a newly designed biodegradable filter cake breakers that could enhance efficiency in filter cake removal. This chelate-based fluid had been proven to effectively dissolved the bridging agent in the filter cake which was calcium carbonate causing the removal of the filter cake to be successful. Besides that, the ability of the chelating agents to provide low corrosion potential made chelating agent as a great alternative for live mineral acids. This provided less aquatic toxicity, more human and environment friendly and readily. However, traditional EDTA cannot performance well in the high-pressure, high-temperature (HPHT) wellbore environments. Therefore, in this study, we explore the use of Titanium-enhanced ethylenediaminetetraacetic acid (Ti-EDTA) as a novel chemical breaker for the decomposition of filter cakes formed in high-pressure, high-temperature (HPHT) wellbore environments. Using ab initio molecular dynamics (AIMD) simulations within the Quantum Espresso framework version 7.2, we examine the molecular interactions, atomic displacements, and thermodynamic behavior of Ti-EDTA in contact with synthetic-based mud (SBM) residues. The simulations track the structural evolution of Ti-EDTA during filter cake decomposition, revealing significant molecular rearrangements and enhanced solubility of the filter cakes. Compared to traditional EDTA-based and silica-based breakers, Ti-EDTA shows improved potential energy and particle mobility, indicative of greater decomposition efficiency. Our results indicate that EDTA resulted in a more negative potential energy of −1035 Ry signifying a stable system, whereas Ti-EDTA simulation yielded a less stable system with a value of −935 Ry energy. The atomic interactions at both levels indicated that Ti-EDTA possesses higher temperature by 16000 K and with a coupled higher potential energy makes an effective chemical to enhance decomposition reactions. For a higher rate of potential energy, temperature and volume, Ti-EDTA molecule best beats EDTA in terms AIMD simulated efficiency. The integration of titanium ions into the EDTA structure significantly boosted the simulation performance, making it a promising candidate for environmentally friendly wellbore cleanup applications. The study provides insights into the molecular mechanisms driving filter cake degradation and sets the stage for future experimental validation of Ti-EDTA’s efficacy in field operations.
@inproceedings{irawan2024molecular, title = {Molecular Dynamic Prognosis for Ti-C10H16N2O8 Filter Cake Decomposition}, author = {Irawan, S and Wayo, DDK and Bayramov, E and Fathaddin, MT and Goliatt, L}, booktitle = {SPE Annual Caspian Technical Conference}, pages = {D021S007R002}, year = {2024}, organization = {SPE}, dimensions = {false}, } - Evolutionary automated radial basis function neural network for multiphase flowing bottom-hole pressure predictionDeivid Campos, Dennis Delali Kwesi Wayo, Rodrigo Barbosa De Santis, and 5 more authorsFuel, 2024
Accurate multiphase flowing bottom-hole pressure prediction within wellbores is a critical requirement to improve tube design and production optimization. Existing models often struggle to achieve reliable accuracy across the full range of operational conditions encountered in oil and gas wells. This can lead to misallocating resources during well design, inefficient production strategies resulting in lost revenue, increased risk of wellbore damage, and poorly informed investment decisions. This research presents a data-driven hybrid approach that uses a Radial Basis Function Neural Network and a Particle Swarm Optimization algorithm to construct an automated hybrid machine learning model. The proposed model was compared with several well-established machine learning models in the literature using the same computational framework. The modeling results demonstrated the superiority of the hybrid approach. The model achieved superior performance with lower errors, as evidenced by a Relative Root Mean Squared Error (RRMSE) of 0.055. Furthermore, the model exhibited a low level of uncertainty throughout the analysis, indicating its high degree of reliability. These findings suggest the proposed data-driven approach offers a robust and practical solution for FBHP prediction in oil and gas wells.
@article{campos2024evolutionary, title = {Evolutionary automated radial basis function neural network for multiphase flowing bottom-hole pressure prediction}, author = {Campos, Deivid and Wayo, Dennis Delali Kwesi and De Santis, Rodrigo Barbosa and Martyushev, Dmitriy A and Yaseen, Zaher Mundher and Duru, Ugochukwu Ilozurike and Saporetti, Camila M and Goliatt, Leonardo}, journal = {Fuel}, volume = {377}, pages = {132666}, year = {2024}, publisher = {Elsevier}, doi = {https://doi.org/10.1016/j.fuel.2024.132666}, dimensions = {false}, } - Filter Cake Neural-Objective Data Modeling and Image OptimizationDennis Delali Kwesi Wayo, Sonny Irawan, Alfrendo Satyanaga, and 3 more authorsSymmetry, 2024
Designing drilling mud rheology is a complex task, particularly when it comes to preventing filter cakes from obstructing formation pores and making sure they can be easily decomposed using breakers. Incorporating both multiphysics and data-driven numerical simulations into the design of mud rheology experiments creates an additional challenge due to their symmetrical integration. In this computational intelligence study, we introduced numerical validation techniques using 498 available datasets from mud rheology and images from filter cakes. The goal was to symmetrically predict flow, maximize filtration volume, monitor void spaces, and evaluate formation damage occurrences. A neural-objective and image optimization approach to drilling mud rheology automation was employed using an artificial neural network feedforward (ANN-FF) function, a non-ANN-FF function, an image processing tool, and an objective optimization tool. These methods utilized the Google TensorFlow Sequential API-DNN architecture, MATLAB-nftool, the MATLAB-image processing tool, and a single-objective optimization algorithm. However, the analysis emanating from the ANN-FF and non-ANN-FF (with neurons of 10, 12, and 18) indicated that, unlike non-ANN-FF, ANN-FF obtained the highest correlation coefficient of 0.96–0.99. Also, the analysis of SBM and OBM image processing revealed a total void area of 1790 M µm2 and 1739 M µm2, respectively. Both SBM and OBM exhibited notable porosity and permeability that contributed to the enhancement of the flow index. Nonetheless, this study did reveal that the experimental-informed single objective analysis impeded the filtration volume; hence, it demonstrated potential formation damage. It is, therefore, consistent to note that automating flow predictions from mud rheology and filter cakes present an alternative intelligence method for non-programmers to optimize drilling productive time.
@article{wayo2024filter, title = {Filter Cake Neural-Objective Data Modeling and Image Optimization}, author = {Wayo, Dennis Delali Kwesi and Irawan, Sonny and Satyanaga, Alfrendo and Kim, Jong and Bin Mohamad Noor, Mohd Zulkifli and Rasouli, Vamegh}, journal = {Symmetry}, volume = {16}, number = {8}, pages = {1072}, year = {2024}, publisher = {MDPI}, doi = {https://doi.org/10.3390/sym16081072}, dimensions = {false}, } - Study on the Interaction of Interfacial Tension Between Water and Oil Surfaces In The Presence of Aluminium Coated With Polyvinylpyrrolidone (PVP) NanoparticlesMuhammad Faris Raffizal, Mohd Zulkifli Mohamad Noor, Mohd Shaiful Zaidi Mat Desa, and 2 more authorsInternational Journal of Nanoelectronics and Materials (IJNeaM), 2024
Applications of nanotechnology are frequently used in the oil and gas sector. However, nanoparticles boost sand consolidation, lower interfacial tension between water and oil, and improve the mobility of trapped oil to increase crude oil recovery in enhanced petroleum recovery (EOR). Using aluminium PVP-coated low-porosity sand packing, the effects of nanoparticles on interfacial tension and water-oil surface contact were examined in this study. The horizontal column was filled with low-porosity sand, and the nanopowders were suspended in deionized water. The four distinct nanoparticle suspension pore volumes (PV) used in this experiment are 0.25, 0.5, 0.75, and 1.0 PV. At the column’s output, the sample is then deposited, and the effluent is analysed and contrasted using IFT and viscosity with a solid viscometer. In this investigation, it was discovered that the water target zone had a very limited pore volume and only received 0.52 PV of injected nanoparticles. The better the oil can be extracted, the lower the viscosity and IFT value. The removal of the oil droplet and increased oil output for EOR could result from the transport of PVP nanoparticles coated with aluminium.
@article{raffizal2024study, title = {Study on the Interaction of Interfacial Tension Between Water and Oil Surfaces In The Presence of Aluminium Coated With Polyvinylpyrrolidone (PVP) Nanoparticles}, author = {Raffizal, Muhammad Faris and Noor, Mohd Zulkifli Mohamad and Desa, Mohd Shaiful Zaidi Mat and Irawan, Sonny and Wayo, Dennis Delali Kwesi}, journal = {International Journal of Nanoelectronics and Materials (IJNeaM)}, volume = {17}, number = {June}, pages = {47--52}, year = {2024}, doi = {https://doi.org/10.58915/ijneam.v17iJune.834}, dimensions = {false}, } - Global Genetic Algorithm for Automating and Optimizing Petroleum Well Deployment in Complex ReservoirsSonny Irawan, Dennis Delali Kwesi Wayo, Alfrendo Satyanaga, and 1 more authorEnergies, 2024
Locating petroleum-productive wells using informed geological data, a conventional means, has proven to be tedious and undesirable by reservoir engineers. The former numerical simulator required a lengthy trial-and-error process to manipulate the variables and uncertainties that lie on the reservoir to determine the best placement of the well. Hence, this paper examines the use of a global genetic algorithm (GA) to optimize the placement of wells in complex reservoirs, rather than relying on gradient-based (GB) methods. This is because GB approaches are influenced by the solution’s surface gradient and may only reach local optima, as opposed to global optima. Complex reservoirs have rough surfaces with high uncertainties, which hinders the traditional gradient-based method from converging to global optima. The explicit focus of this study was to examine the impact of various initial well placement distributions, the number of random solution sizes and the crossover rate on cumulative oil production, the optimization of the synthetic reservoir model created by CMG Builder, CMOST, and IMEX indicated that using a greater number of random solutions led to an increase in cumulative oil production. Despite the successful optimization, more generations are required to reach the optimal solution, while the application of GA on our synthetic model has proven efficient for well placement; however, different optimization algorithms such as the improved particle swarm (PSO) and grey wolf optimization (GWO) algorithms could be used to redefine well-placement optimization in CMG.
@article{irawan2024global, title = {Global Genetic Algorithm for Automating and Optimizing Petroleum Well Deployment in Complex Reservoirs}, author = {Irawan, Sonny and Wayo, Dennis Delali Kwesi and Satyanaga, Alfrendo and Kim, Jong}, journal = {Energies}, volume = {17}, number = {9}, pages = {2151}, year = {2024}, publisher = {MDPI}, doi = {https://doi.org/10.3390/en17092151}, dimensions = {false}, }
2023
- Factors affecting drilling incidents: Prediction of suck pipe by XGBoost modelTalgat Kizayev, Sonny Irawan, Javed Akbar Khan, and 4 more authorsEnergy Reports, 2023
The unproductive time is very high due to drill string jamming. So the main objective of this research is to determine the influences of the parameters in the accidents of stuck pipes using the construction of XGBoost models. To develop the model, drilling parameters are taken from daily drilling reports of the construction of 30 wells in the West Qurna field, Iraq. The data includes well drilling reports from 2013 to 2020. The results show that the factors such as Measured depth (MD), Rate of penetration (ROP), 10-sec gel (GEL1), Plastic Viscosity (PV), Mud weight (MW), Yield point (YP) contribute positively to the model predictions. In contrast, the factors such as Flow rate (FR), Rotation per minute (RPM), Filtrate (API/HPHT-FILTR and Bottom hole assembly (BHA) length has a negative contribution to the final model predictions. This research concluded that pipe sticking in the borehole is primarily due to inclination, penetration rate, and flow rate. This study is useful in the drilling of any field.
@article{kizayev2023factors, title = {Factors affecting drilling incidents: Prediction of suck pipe by XGBoost model}, author = {Kizayev, Talgat and Irawan, Sonny and Khan, Javed Akbar and Khan, Sumeer Ahmad and Cai, Baoping and Zeb, Nazia and Wayo, Dennis Delali Kwesi}, journal = {Energy Reports}, volume = {9}, pages = {270--279}, year = {2023}, publisher = {Elsevier}, doi = {https://doi.org/10.1016/j.egyr.2023.03.083}, dimensions = {false}, } - Modelling and Simulating Eulerian Venturi Effect of SBM to Increase the Rate of Penetration with Roller Cone Drilling BitDennis Delali Kwesi Wayo, Sonny Irawan, Alfrendo Satyanaga, and 1 more authorEnergies, 2023
Drilling bits are essential downhole hardware that facilitates drilling operations in high-pressure, high-temperature regions and in most carbonate reservoirs in the world. While the drilling process can be optimized, drilling operators and engineers become curious about how drill bits react during rock breaking and penetration. Since it is experimentally expensive to determine, the goal of the study is to maximize the rate of penetration by modeling fluid interactions around the roller cone drilling bit (RCDB), specifying a suitable number of jet nozzles and venturi effects for non-Newtonian fluids (synthetic-based muds), and examining the effects of mud particles and drill cuttings. Ansys Fluent k-epsilon turbulence viscous model, a second order upwind for momentum, turbulent kinetic energy, and dissipation rate, were used to model the specified 1000 kg/m3 non-Newtonian fluid around the roller cone drill bit. The original geometry of the nozzles was adapted from a Chinese manufacturer whose tricone had three jet nozzles. The results of our six redesigned jet nozzles (3 outer, 39.12 mm, and 3 proximal, 20 mm) sought to offer maximum potential for drilling optimization. However, at a pressure of 9.39 × 104 Pa, the wellbore with particle sizes between 0.10 mm and 4.2 mm drill cuttings observed an improved rate of penetration with a rotation speed of 150 r/min.
@article{wayo2023modelling, title = {Modelling and Simulating Eulerian Venturi Effect of SBM to Increase the Rate of Penetration with Roller Cone Drilling Bit}, author = {Wayo, Dennis Delali Kwesi and Irawan, Sonny and Satyanaga, Alfrendo and Abbas, Ghulam}, journal = {Energies}, volume = {16}, number = {10}, pages = {4185}, year = {2023}, publisher = {MDPI}, doi = {https://doi.org/10.3390/en16104185}, dimensions = {false}, } - Data-Driven Fracture Morphology Prognosis from High Pressured Modified Proppants Based on Stochastic-Adam-RMSprop Optimizers; tf. NNR StudyDennis Delali Kwesi Wayo, Sonny Irawan, Alfrendo Satyanaga, and 1 more authorBig Data and Cognitive Computing, 2023
Data-driven models with some evolutionary optimization algorithms, such as particle swarm optimization (PSO) and ant colony optimization (ACO) for hydraulic fracturing of shale reservoirs, have in recent times been validated as one of the best-performing machine learning algorithms. Log data from well-logging tools and physics-driven models is difficult to collate and model to enhance decision-making processes. The study sought to train, test, and validate synthetic data emanating from CMG’s numerically propped fracture morphology modeling to support and enhance productive hydrocarbon production and recovery. This data-driven numerical model was investigated for efficient hydraulic-induced fracturing by using machine learning, gradient descent, and adaptive optimizers. While satiating research curiosities, the online predictive analysis was conducted using the Google TensorFlow tool with the Tensor Processing Unit (TPU), focusing on linear and non-linear neural network regressions. A multi-structured dense layer with 1000, 100, and 1 neurons was compiled with mean absolute error (MAE) as loss functions and evaluation metrics concentrating on stochastic gradient descent (SGD), Adam, and RMSprop optimizers at a learning rate of 0.01. However, the emerging algorithm with the best overall optimization process was found to be Adam, whose error margin was 101.22 and whose accuracy was 80.24% for the entire set of 2000 synthetic data it trained and tested. Based on fracture conductivity, the data indicates that there was a higher chance of hydrocarbon production recovery using this method.
@article{wayo2023data, title = {Data-Driven Fracture Morphology Prognosis from High Pressured Modified Proppants Based on Stochastic-Adam-RMSprop Optimizers; tf. NNR Study}, author = {Wayo, Dennis Delali Kwesi and Irawan, Sonny and Satyanaga, Alfrendo and Kim, Jong}, journal = {Big Data and Cognitive Computing}, volume = {7}, number = {2}, pages = {57}, year = {2023}, publisher = {MDPI}, doi = {https://doi.org/10.3390/bdcc7020057}, dimensions = {false}, }
2022
- A CFD validation effect of YP/PV from laboratory-formulated SBMDIF for productive transport load to the surfaceDennis Delali Kwesi Wayo, Sonny Irawan, Mohd Zulkifli Bin Mohamad Noor, and 3 more authorsSymmetry, 2022
Several technical factors contribute to the flow of cuttings from the wellbore to the surface of the well, some of which are fundamentally due to the speed and inclination of the drill pipe at different positions (concentric and eccentric), the efficacy of the drilling mud considers plastic viscosity (PV) and yield point (YP), the weight of the cuttings, and the deviation of the well. Moreover, these overlaying cutting beds breed destruction in the drilling operation, some of which cause stuck pipes, reducing the rate of rotation and penetration. This current study, while it addresses the apropos of artificial intelligence (AI) with symmetry, employs a three-dimensional computational fluid dynamic (CFD) simulation model to validate an effective synthetic-based mud-drilling and to investigate the potency of the muds’ flow behaviours for transporting cuttings. Furthermore, the study examines the ratio effects of YP/PV to attain the safe transport of cuttings based on the turbulence of solid-particle suspension from the drilling fluid and the cuttings, and its velocity–pressure influence in a vertical well under a concentric and eccentric position of the drilling pipe. The resulting CFD analysis explains that the YP/PV of SBM and OBM, which generated the required capacity to suspend the cuttings to the surface, are symmetric to the experimental results and hence, the position of the drill pipe at the concentric position in vertical wells required a lower rotational speed. A computational study of the synthetic-based mud and its potency of not damaging the wellbore under an eccentric drill pipe position can be further examined.
@article{wayo2022cfd, title = {A CFD validation effect of YP/PV from laboratory-formulated SBMDIF for productive transport load to the surface}, author = {Wayo, Dennis Delali Kwesi and Irawan, Sonny and Bin Mohamad Noor, Mohd Zulkifli and Badrouchi, Foued and Khan, Javed Akbar and Duru, Ugochukwu I}, journal = {Symmetry}, volume = {14}, number = {11}, pages = {2300}, year = {2022}, publisher = {MDPI}, doi = {https://doi.org/10.3390/sym14112300}, dimensions = {false}, } - CFD Validation for Assessing the Repercussions of Filter Cake Breakers; EDTA and SiO2 on Filter Cake Return PermeabilityDennis Delali Kwesi Wayo, Sonny Irawan, Javed Akbar Khan, and 1 more authorApplied Artificial Intelligence, 2022
Drill-in-fluids create what are known as filter cakes. Filter cakes, in some cases, lead to well abandonment because they prevent hydrocarbons from flowing freely from the formation into the wellbore. Cake removal is essential to avoid formation damage. A previous study on filter cake breakers was considered for computational fluid dynamic (CFD) validation. Matlab-CFD and Navier-Stokes equations aimed at predicting and validating visual, multiphase flow under finite element analysis (FEA). The interactions of separate chemical breakers and drill-in-fluid such as ethylenediaminetetraacetic acid (EDTA), silica-nanoparticle (SiO2), and biodegradable synthetic-based mud drill-in-fluid (BSBMDIF) were monitored under a particle size distribution, viscosity, density, and pressure. Predicting return permeability of filter cake was considered under a simple filtration process. The particles’ deposition created pore spaces between them; barite 74 μm, nano-silica 150 nm, and EDTA 10 μm generally closed up the pores of the filtration medium. Under extreme drilling conditions, barite formed thicker regions, and EDTA chemical properties easily disjointed these particles, while SiO2 entirely did not. The experimented results of (EDTA) and SiO2 for return permeability were in full force agreeable with the 2D simulation. A hybrid computational analysis considering CFD under discrete element analysis and neural network can be employed for further research validations.
@article{wayo2022cfe, title = {CFD Validation for Assessing the Repercussions of Filter Cake Breakers; EDTA and SiO2 on Filter Cake Return Permeability}, author = {Wayo, Dennis Delali Kwesi and Irawan, Sonny and Khan, Javed Akbar and Fitrianti}, journal = {Applied Artificial Intelligence}, volume = {36}, number = {1}, pages = {2112551}, year = {2022}, publisher = {Taylor \& Francis}, doi = {https://doi.org/10.1080/08839514.2022.2112551}, dimensions = {false}, } - Computational analysis for optimum multiphase flowing bottom-hole pressure predictionUgochukwu I Duru, Dennis Delali Kwesi Wayo, Reginald Ogu, and 2 more authorsTransylvanian Review, 2022
Computer intelligent models are the order of the day for the manipulation of data to better understand the trend of complex situations under the questioned industry. The petroleum engineering is faced with multiple datasets from production logging tools and predictive analysis without these computer intelligent tools can be devastating. Errors of margins under these circumstances cannot be easily prevented, which may lead to some biases in the decision-making processes, thereby affecting the cost of operations and services in the industry. This study used an open-source dataset from a production well logging tool to evaluate and affirm the accuracy of a computer intelligent model, suitable for processing complex problems. However, an artificial neural network under the feedforward function and a model fitting-multilinear regression were used for this predictive analysis. Conclusively, the predictive analysis, whiles considering the coefficient of determination for these two models resulted that, the artificial neural network- feedforward function was better in predicting the flowing bottom-hole pressures than the multilinear regression. Multiphase flow under bottom-hole pressures can further be computed using CFD to determine variations of pressure drops, predicting flow patterns and geometry to enhance prudent decision-making analysis.
@article{duru2022computational, title = {Computational analysis for optimum multiphase flowing bottom-hole pressure prediction}, author = {Duru, Ugochukwu I and Wayo, Dennis Delali Kwesi and Ogu, Reginald and Cyril, Chindera and Nnani, Happiness}, journal = {Transylvanian Review}, volume = {30}, number = {2}, year = {2022}, dimensions = {false}, }