Solomon Asante-Okyere

• PhD
• University of Mines and Technology

Yield point (YP) is an essential rheological property of drilling mud that influences the ability of the mud to lift well cuttings from the annulus to the surface, impacting the overall drilling efficiency. Despite its significance, YP is typically measured only once or twice a day using complex rheometers. On the other hand, a simple field equipme...

Shale deposits from Belata Formation outcrops were examined geochemically and mineralogically to establish their paragenesis and, subsequently, to identify factors that govern their depositional environment. Fifty representative samples were examined through SEM/EDX, XRD and XRF. The findings show the mineralogical composition was abundant in quart...

Fused deposition modelling (FDM) is a popular additive manufacturing technique due to its low cost of producing complex parts. The quality of print part from the FDM process in terms of energy demand, mechanical and physical properties are influenced by its process parameters. The task of using artificial intelligence to better understand the influ...

Cementing is one of the most critical drilling operations in the oil and gas industry. However, the production and utilization of cement is a major emitter of CO 2 into the atmosphere. Processes and technologies that can reduce the overall carbon footprint of the oil and gas industry while meeting the increasing global demand for energy are of key...

A fundamental parameter in the exploration and development of unconventional shale reservoirs is total organic carbon (TOC). To achieve reliable TOC values, it requires a labour intensive and time-consuming laboratory experiment. On the other hand, models have been proposed using geophysical well logs as input variables with little attention paid t...

There are instances in well logging operations where log response can be missing or inaccurate for a specific depth of interest due to wellbore conditions such as wellbore size, wellbore rugosity and mud cake effects. The conventional approach is to rerun the logs at definite depths, however, this remedial technique is costly, time-consuming and pr...

Lithology identification is a fundamental activity in oil and gas exploration. The application of artificial intelligence (AI) is currently being adopted as a state-of-the-art means of automating lithology identification. One aspect of this AI approach is the application of population search algorithms to optimise hyperparameters for enhanced predi...

The conventional quantitative lithofacies palaeogeography mapping method is summarized as single-factor analysis and multiple factor comprehensive. It has been widely accepted and very often used in the compilation of lithofacies palaeogeography maps. However, specifically in manual mapping procedures, different analysis results are often obtained...

Vitrinite reflectance (Ro) analysis is a maturity indication parameter for oil or gas prone source rocks in evaluating hydrocarbon potentials. As a result of challenges in Ro calculations from pyrolysis, finding a way to estimate the maturity of source rocks has been an interesting subject for researchers. The is a current need to improve the Ro ca...

Flowing bottom hole pressure (FBHP) is an indication of the fluid pressure in a porous reservoir under production. FBHP is obtained using a permanent gauge, well-testing analysis, or developing a mechanistic/correlation model. These conventional methods are costly and can be unreliable in the case of mechanistic/correlation approach. In line with t...

While there have been various attempts in establishing a model that can predict the flammability characteristics of polymers based on their molecular structure, artificial intelligence (AI) presents a promising alternative in tackling this pressing issue. Therefore, a novel approach of adopting AI methods, extreme learning machines (ELMs) and group...

Recent advancement in computing capabilities has brought to light the application of machine learning methods in estimating geochemical data from well logs. The widely employed artificial neural network (ANN) has intrinsic problems in its application. Therefore, the objective of this study was to present a group method of data handling (GMDH) neura...

Unconventional resources, such as shale oil and gas, are currently regarded as an essential resource in the face of depleting conventional hydrocarbon reserves. In line with this, the accurate determination of the hydrocarbon potential of a shale reservoir is critical and relies in part, on the total organic carbon (TOC) content. However, there exi...

Water saturation is imperative in the evaluation of hydrocarbon reserves available. However, it is challenging to accurately determine the water saturation of complex reservoirs using conventional techniques. Also, most computational intelligence methods developed to estimate water saturation have neglected the relationship that can exist between i...

Permeability is an important petrophysical parameter that controls the fluid flow within the reservoir. Estimating permeability presents several challenges due to the conventional approach of core analysis or well testing, which are expensive and time-consuming. On the contrary, artificial intelligence has been adopted in recent years in predicting...

Flammability analysis of extruded polystyrene (XPS) has become crucial due to its utilization as insulation material for energy efficient buildings. Using the Kissinger-Akahira-Sunose and Flynn-Wall-Ozawa methods, the degradation kinetics of two pure XPS from the local market, red and grey ones, were obtained from the results of thermogravity analy...

The significant body of research on lithology identification in recent years has laid emphasis on the improvement of classification performance using hybrid machine learning methods. To the best of our knowledge, a hybrid lithology classification model that integrates clustering results of well log data has not been developed. This study, therefore...

Predictions of both combustible material flammability and heat release parameters have been long goals in fire safety research, for its complex heat, mass transfer and chemical reaction process in gas phase. In this study, neural network method is employed to predict materials flammability considering its wide application in predicting key properti...

Although the group method of data handling (GMDH) is a self-organizing metaheuristic neural network capable of developing a classification function using influential input variables, the results can be improved by using some pre-processing steps. In this paper, we propose a joint principal component analysis (PCA) and GMDH (PCA-GMDH) classifier met...

In this paper, a new predictive model based on Gaussian process regression (GPR) that does not require iterative tuning of user-defined model parameters has been proposed to determine reservoir porosity and permeability. For this purpose, the capability of GPR was appraised statistically for predicting porosity and permeability of the southern basi...

Flammability studies are conducted to evaluate the behavior of materials exposed to fire. In this study, microscale combustion calorimetry (MCC) and cone calorimetry methods were applied to acquire the flammability characteristics of red and grey extruded polystyrene (XPS) samples. To understand the effect of changes between parameters, Pearson’s c...

The capability of artificial neural networks in predicting microscale combustion calorimeter (MCC) parameters of polymethyl methacrylate (PMMA) was carried out in this study. Using values of sample mass and corresponding heating rate, feed forward back propagation (FFBP) and generalized regression neural network (GRNN) models were developed to pred...

Coal has been the primary source of energy in China due to its abundance, with 55% of its consumption in power generation. Carbon capture and sequestration (CCS) presents an alternate solution as one of the clean coal utilization technologies being developed in China. This paper used life cycle assessment to compare environmental impacts of carbon...