Laboratory Characterization of crude oil and sandstone reservoir for Chemical Enhanced Oil Recovery

Purpose Because of the increasing global oil demand, efforts have been made to further extract oil using chemical enhanced oil recovery (CEOR) methods. However, unlike water flooding, understanding the physicochemical properties of crude oil and its sandstone reservoir makeup is the first step before embarking to CEOR projects. These properties play major roles in the area of EOR technologies and are important for the development of reliable chemical flooding agents; also, they are key parameters used to evaluate the economic and technical feasibilities of production and refining processes in the oil industries. Consequently, this paper aims to investigate various important physicochemical properties of crude oil (specific gravity; American Petroleum Institute [API]; viscosity; pour point; basic sediment and water; wax; and saturate, aromatic, resins and asphaltenes components) and sandstone reservoir makeup (porosity, permeability, bulk volume and density, grain volume and density, morphology and mineral composition and distributions) obtained from Malaysian oil field (MOF) for oil recovery prediction and design of promising chemical flooding agents. Design/methodology/approach Three reservoir sandstones from different depths (CORE 1; 5601, CORE 2; 6173 and CORE 3; 6182 ft) as well as its crude oil were obtained from the MOF, and various characterization instruments, such as high temperature gas chromatography and column chromatography for crude’s fractions identification; GC-simulated distillation for boiling point distribution; POROPERM for porosity and permeability; CT-Scan and scanning electron microscopy-energy dispersive X-ray for morphology and mineral distribution; wax instrument (wax content); pour point analyser (pour point); and visco-rheometre (viscosity), were used for the characterizations. Findings Experimental data gathered from this study show that the field contains low viscous (0.0018-0.014 Pa.s) sweet and light-typed crude because of low sulfur content (0.03 per cent), API gravity (43.1o), high proportion of volatile components (51.78 per cent) and insignificant traces of heavy components (0.02 per cent). Similarly, the rock permeability trend with depth was found in the order of CORE 1 &lt; CORE 2 &lt; CORE 3, and other parameters such as pore volume (Vp), bulk volume (Vb) and grain volume (Vg) also decrease in general. For grain density, the variation is small and insignificant, but for bulk density, CORE 2 records lower than CORE 3 by more than 1 per cent. In the mineral composition analysis, the CORE 2 contains the highest identified mineral content, with the exception of quarts where it was higher in the CORE 3. Thus, a good flow crude characteristic, permeability trend and the net mineral concentrations identified in this reservoir would not affect the economic viability of the CEOR method and predicts the validation of the MOF as a potential field that could respond to CEOR method successfully. Originality/value This paper is the first of its kind to combine the two important oil field properties to scientifically predict the evaluation of an oil field (MOF) as a step forward toward development of novel chemical flooding agents for application in EOR. Hence, information obtained from this paper would help in the development of reliable chemical flooding agents and designing of EOR methods. </jats:sec

Effects of Promoter’s Composition on the Physicochemical Properties of Cu/ZnO/Al₂O₃-ZrO₂ Catalyst

Cu/ZnO catalysts were synthesized via an impregnation method on an Al2O3-ZrO2 support and modified by the addition of manganese and niobium as promoters. The effect of the selected promoters on the physicochemical properties and performance toward the hydrogenation of CO2 to methanol are presented in this paper. The Mn and Nb promoters improved the reducibility of the catalyst as evidenced by the shifting of the H2-TPR peaks from 315 °C for the un-promoted catalyst to 284 °C for the Mn- and Nb-promoted catalyst. The catalytic performance in a CO2 hydrogenation reaction was evaluated in a fixed-bed reactor system at 22.5 bar and 250 °C for 5 h. Amongst the catalysts investigated, the catalyst with equal ratio of Mn and Nb promoters exhibited the smallest particle size of 6.7 nm and highest amount of medium-strength basic sites (87 µmol/g), which resulted in the highest CO2 conversion (15.9%) and methanol selectivity (68.8%)

A Novel Empirical and Deep Ensemble Super Learning Approach in Predicting Reservoir Wettability via Well Logs

Accurately measuring wettability is of the utmost importance because it influences several reservoir parameters while also impacting reservoir potential, recovery, development, and management plan. As such, this study proposes a new formulated mathematical model based on the correlation between the Amott-USBM wettability measurement and field NMR T2LM log. The exponential relationship based on the existence of immiscible fluids in the pore space had a correlation coefficient of 0.95. Earlier studies on laboratory core wettability measurements using T2 distribution as a function of increasing water saturation were modified to include T2LM field data. Based on the trends observed, water-wet and oil-wet conditions were qualitatively identified. Using the mean T2LM for the intervals of interest and the formulated mathematical formula, the various wetting conditions in existence were quantitatively measured. Results of this agreed with the various core wettability measurements used to develop the mathematical equation. The results expressed the validity of the mathematical equation to characterise wettability at the field scale. With the cost of running NMR logs not favourable, and hence not always run, a deep ensemble super learner was employed to establish a relationship between NMR T2LM and wireline logs. This model is based on the architecture of a deep learning model and the theoretical background of ensemble models due to their reported superiority. The super learner was developed using nine ensemble models as base learners. The performance of nine ensemble models was compared to the deep ensemble super learner. Based on the RMSE, R2, MAE, MAPD and MPD the deep ensemble super learner greatly outperformed the base learners. This indicates that the deep ensemble super learner can be used to predict NMR T2LM in the field. By applying the methodology and mathematical formula proposed in this study, the wettability of reservoirs can be accurately characterised as illustrated in the field deployment.publishedVersio

Enhanced catalyst dispersion and structural control of Co3O4-silica nanocomposites by rapid thermal processing

We synthesized cobalt tetroxide (Co3O4) silica nanocomposites based on the conventional tetraethyl orthosilicate (TEOS) monomer and ethoxy polysiloxane (ES40) oligomer by sol-gel chemistry coupled with rapid thermal process (RTP). The physicochemical properties and structural formation of cobalt oxide silica nanocomposites were comprehensive characterized. By using ES40, well-controlled, homogeneous nanoparticle dispersion and size of Co3O4 with 5 nm within the silica matrix were achieved leading to fractal-like morphology. The concentration of the Co3O4 nanocatalyst was also significantly enhanced by more than 50 folds. Fenton-like HCO3 −/H2O2 catalytic system using acid orange 7 and nanocomposites was examined for organic degradation. 98% AO7 and naphthalene intermediates degradation efficiency was achieved after 20 min with ES40-derived catalyst, which was three to ten folds faster than that of the TEOS-derived catalyst and the commercial Co3O4 catalyst. The combined use of ES40 sol-gel and RTP enabled a simple way to nanomaterial preparation and lowers overall processing time

Effect of pH, Acid and Thermal Treatment Conditions on Co/CNT Catalyst Performance in Fischer–Tropsch Reaction

Multiwalled carbon nanotubes (CNT) supported cobalt oxide was prepared as a catalyst by strong electrostatic adsorption (SEA) method. The CNT support was initially acid- and thermal-treated in order to functionalize the support to uptake more Co clusters. The Co/CNT were characterized by a range of analytical methods including transmission electron microscopy (TEM), temperature programmed reduction with hydrogen (H2-TPR), X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), Raman spectroscopy, atomic absorption spectroscopy (AAS), Zeta sizer particle size analysis and Brunauer-Emmett-Teller (BET) surface area analysis. TEM images showed cobalt particles were highly dispersed and impregnated at both exterior and interior walls of the CNT support with a narrow particle size distribution of 6-8 nm. In addition, the performance of the synthesized Co/CNT catalyst was tested using Fischer-Tropsch synthesis (FTS) reaction which was carried out in a fixed-bed micro-reactor. H2-TPR profiles indicated the lower reduction temperature of 420 °C was required for the FTS reaction. The study revealed that cobalt is an effective metal for Co/CNT catalysts at pH 14 and at 900 °C calcination temperature. Furthermore, FTS reaction results showed that CO conversion and C5+ selectivity were recorded at 58.7% and 83.2% respectively, which were higher than those obtained using a Co/CNT catalyst which pre-treated at a lower thermal treatment temperature and pH. © 2019 by the authors

Effects of Cobalt Loading, Particle Size, and Calcination Condition on Co/CNT Catalyst Performance in Fischer–Tropsch Reactions

The strong electrostatic adsorption (SEA) method was applied to the synthesis of a cobalt (Co) catalyst on a multi-walled carbon nanotube (CNT) support. In order to uptake more of the cobalt cluster with higher dispersion, the CNT was functionalized via acid and thermal treatment. The Co/CNT catalyst samples were characterized by a range of methods including the Brunauer&ndash;Emmet&ndash;Teller (BET) surface area analyzer, transmission electron microscopy (TEM), X-ray powder diffraction (XRD) analysis, atomic absorption spectroscopy (AAS), and H2-temperature programmed reduction (H2-TPR) analysis. The data from the TEM images revealed that the catalyst was highly dispersed over the external and internal walls of the CNT and that it demonstrated a narrow particle size of 6&ndash;8 nm. In addition, the data from the H2-TPR studies showed a lower reduction temperature (420 &deg;C) for the pre-treated catalyst samples. Furthermore, a Fischer&ndash;Tropsch synthesis (FTS) reaction was chosen to evaluate the Co/CNT catalyst performance by using a fixed-bed microreactor at different parameters. Finally finding the optimum value of the cobalt loading percentage, particle size, and calcination conditions of Co/CNT catalyst resulted in a CO conversion and C5+ selectivity of 58.7% and 83.2%, respectively