On Improving Water-Based Drilling Mud Swelling Control Using Modified Poly(Vinyl Alcohol)s.

One of the most challenging issues when drilling under high-temperature, high-pressure (HT/HP) conditions is wellbore instability caused by clay swelling and fluid loss of the drilling mud. One of the most difficult issues when drilling under high-temperature, high-pressure (HT/HP) conditions is wellbore instability caused by clay swelling and fluid loss in drilling mud. Two modified PVOHs, nonionic and cationic polymers made from sodium bentonite clay and deionized water at concentrations of 0.08, 0.28, and 0.49 wt.%, were introduced to WBM percent. A series of specific gravity and mud rheology experiments at 25, 55, and 85 °C indicated that both values drop monotonically with increasing temperature, regardless of PVOH addition or concentration. A temperature increase of 30 °C decreases the mud viscosity of WBM (without PVOH) by 18% from its starting value, on average. Only 0.1% of cationic and nonionic polymer reduces viscosity by 10% and 0%, respectively. Experimenting with mud samples for 5 h revealed that adding nonionic polymers enhances mud filtration by up to 34.7%, 1.25 times more than that achieved from cationic polymers under the same filtration circumstances. Increasing the filtration temperature moderately affects mud cake generation due to increased mud swelling index and preferential adsorption by nonionic polymer. The latter observation was corroborated by determining the polymer content of the filtrates. Therefore, it was shown that nonionic polymers adsorbed more (118.9 mg/g) than cationic polymers (84.51 mg/g). Increased filtration temperature moderately affects mud cake generation due to increased mud swelling index and preferential adsorption by nonionic polymer. The latter observation was corroborated by testing the filtrates for the polymer content. As a result, it was discovered that nonionic polymer adsorbed more (118.9 mg/g) than cationic polymer (84.51 mg/g). Thermogravimetry analysis (TGA) finally tested the thermal stability of polymers.

Experimental Study on Enhanced Methane Detection Using an MEMS-Pyroelectric Sensor Integrated with a Wavelet Algorithm.

An optical sensing approach that balances portability with cost efficiency has been designed for the reliable monitoring of fugitive methane (CH4) emissions. Employing a LiTaO3-based pyroelectric detector integrated with micro-electro-mechanical systems and a broad infrared source, the developed gas sensor adeptly measured CH4 concentrations with a low limit of detection of about 5.6 ppmv and showed rapid response times with t90 consistently under 3 s. Notably, the novelty of our method lies in its precise control and reduction of CH4 levels, enhanced by wavelet denoising. This technique, optimized through meticulous grid search, effectively mitigated noise interference noticeable at CH4 levels below 10 ppmv. Postdenoising, nonlinear regression analyses based on the modified Beer-Lambert equation returned R2 values of 0.985 and 0.982 for the training and validation sets, respectively. In conclusion, this gas sensor has been shown to be able to meet the requirements for early warning of CH4 leakage on the surface in various carbon capture, utilization, and storage projects such as enhanced oil or gas recovery projects using CO2 injection.

New Method to Predict the Viscosity of Bitumen Diluted with Light Oil Using a Modified Van Der Wijk Model under Reservoir Temperature and Pressure.

In this study, we introduce a new method for the prediction of the viscosity of bitumen diluted with light oil under reservoir temperature and pressure. This two-step method works as follows: first, predicting the bitumen viscosity under reservoir temperature and pressure using the classical Mehrotra and Svrcek model, and then subsequently using it in the modified Van Der Wijk (MVDM) model. This model formed from the modification of the original Van Der Wijk model was developed from the consideration of the interactions between like molecules in different binary components of the mixture. In this study, the bitumen viscosity was predicted with an average absolute deviation percentage (AAD%) of 3.86. The accuracy of the MVDM was investigated from the experimental results obtained from the rheological studies of three binary mixtures of light oil (API 32°) and bitumen (API 7.39°). Dead oils were mixed on a mass fraction basis. The viscosity was measured at a temperature range of 45-110 °C and a pressure range of 0.1-6 MPa. For comparison purposes, a reworked Van Der Wijk model (RVDM) was used in the same method and compared to the MVDM. The latter was more accurate than the RVDM with AAD% values of 8.88, 8.02, and 5.07 in predicting the viscosity of the three mixtures of 25, 32.5, and 50% bitumen with light oil. On the other hand, the RVDM had AAD% values of 12.42, 11.43, and 7.87 for the same mixtures, respectively. The applicability of this method was further verified by comparing its accuracy to another reported method using published data and it was found that the MVDM had AAD% values of 1.86, 6.55, and 2.823 when predicting the viscosities of the three mixtures under reservoir temperature and pressure conditions.

Formation Damage Induced by Water-Based Alumina Nanofluids during Enhanced Oil Recovery: Influence of Postflush Salinity.

Injecting nanofluids (NFs) has been proven to be a potential method to enhance oil recovery. Stranded oil is produced by wettability alteration where nanoparticles form a wedge film on pore wall surfaces, which is thought to shrink the pore space of the reservoir. Furthermore, ensuring the stability of the injected NF during the application is a major challenge. A low permeability reservoir and salinity of water make the response of NF injection to the formation damage more difficult. This article, therefore, studied the formation damage induced by the injection of alumina nanofluids (Al-NFs) in a relatively low permeability (7.1 mD) sandstone core. The salinity of the postflush water was also considered to mitigate the destructive impact. Al-NF was formulated by dispersing alumina nanoparticles (Al-NPs) in an aqueous solution of sodium dodecylbenzene sulfonate (SDBS) at its critical micelle concentration (CMC, 0.1 wt %). The formation damage, inherent to Al-NF injection, was evaluated by core-flooding tests. The assays consisted of the injection of 1 PV Al-NF (0.05 wt %) at the trail of which postflush at different salinities was flooded. The study found that the salinity of the postflush has an effect on the formation damage and oil recovery factor (RF). A chase water with a salinity concentration of 3 wt % sodium chloride (NaCl) produced an RF of 8.7% compared to a base case of water-flooding with a pressure drop of up to 13 MPa across the core (70 mm in length). These results pertained to the deposition of Al-NPs at the injection end. However, lowering the postflush salinity to 1 wt % NaCl mitigated the formation damage as evidenced by the decrease in pressure (35%) and an increase in RF to 17.2%.

Application of artificial neural network for predicting the performance of CO2 enhanced oil recovery and storage in residual oil zones.

Residual Oil Zones (ROZs) become potential formations for Carbon Capture, Utilization, and Storage (CCUS). Although the growing attention in ROZs, there is a lack of studies to propose the fast tool for evaluating the performance of a CO2 injection process. In this paper, we introduce the application of artificial neural network (ANN) for predicting the oil recovery and CO2 storage capacity in ROZs. The uncertainties parameters, including the geological factors and well operations, were used for generating the training database. Then, a total of 351 numerical samples were simulated and created the Cumulative oil production, Cumulative CO2 storage, and Cumulative CO2 retained. The results indicated that the developed ANN model had an excellent prediction performance with a high correlation coefficient (R2) was over 0.98 on comparing with objective values, and the total root mean square error of less than 2%. Also, the accuracy and stability of ANN models were validated for five real ROZs in the Permian Basin. The predictive results were an excellent agreement between ANN predictions and field report data. These results indicated that the ANN model could predict the CO2 storage and oil recovery with high accuracy, and it can be applied as a robust tool to determine the feasibility in the early stage of CCUS in ROZs. Finally, the prospective application of the developed ANN model was assessed by optimization CO2-EOR and storage projects. The developed ANN models reduced the computational time for the optimization process in ROZs.

Effects of temperature gradient and particle size on self-ignition temperature of low-rank coal excavated from inner Mongolia, China.

This study investigates the effects of temperature gradient and coal particle size on the critical self-ignition temperature T CSIT of a coal pile packed with low-rank coal using the wire-mesh basket test to estimate T CSIT based on the Frank-Kamenetskii equation. The values of T CSIT, the temperature gradient and the apparent activation energy of different coal pile volumes packed with coal particles of different sizes are measured. The supercriticality or subcriticality of the coal is assessed using a non-dimensional index IHR based on the temperature gradient at the temperature cross-point between coal and ambient temperatures for coal piles with various volumes and particle sizes. The critical value IHRC at the boundary between supercriticality and subcriticality is determined as a function of pile volume. The coal status of supercritical or subcritical can be separated by critical value of IHR as a function of pile volume. Quantitative effects of coal particle size on T CSIT of coal piles are measured for constant pile volume. It can be concluded that a pile packed with smaller coal particles is more likely to undergo spontaneous combustion, while the chemical activation energy is not sensitive to coal particle size. Finally, the effect of coal particle size on T CSIT is represented by the inclusion of an extra term in the equation giving T CSIT for a coal pile.

Field Study on Correlation between CO2 Concentration and Surface Soil CO2 Flux in Closed Coal Mine Goaf.

Self-heating of coal mine goaf or shallow coal seams can release an outbreak of unimaginable pollution disaster under suitable circumstances. As an indicator gas, CO2 is always used to determine the coal spontaneous combustion state during the self-heating process. Based on this, the paper investigated the influence of abandoned coal mine goaf CO2 on the surface environment by measuring the CO2 concentration in the borehole connected to the goaf and CO2 flux on the soil surface. Furthermore, rainfall and atmospheric temperature effects are discussed to illustrate the correlation between the CO2 concentration and surface soil CO2 flux in the closed mine goaf. Subsequently, the tracer gas experimental method is employed to analyze the effect of air leakage from an open-pit slope on CO2 flux. The experimental results demonstrated that the distribution of CO2 concentration in the borehole confirms the continuous diffusion of goaf CO2 onto the surface. The value of CO2 flux in the goaf is significantly higher than that of a normal area. Temperature is one of the primary factors that affect the CO2 flux on the field. Air leakage from the slope promotes the surface soil-overlying goaf CO2 diffusion. The study provides important reference data for the assessment of the mining area field environment and the determination of the spontaneous combustion risk of the residual coal in the goaf.

Estimating a baseline of soil CO2 flux at CO2 geological storage sites.

This study aims to determine a baseline for natural soil carbon dioxide (CO2) flux at the surface based on long-term field measurements, with the ultimate purpose to detect the gas leakage at CO2 geological storage sites. CO2 surface monitoring is a tool that measures the safety and effectiveness of CO2 capture and storage (CCS), a technology which is believed to be a reliable approach to mitigate the CO2 emission. However, the fluctuations of naturally occurring CO2 in soil layers complicate the leakage detection as the soil connects both the underground layers and the atmosphere. In this regard, this study not only investigates the natural surface CO2 flux behavior but also develops an equation to estimate the surface CO2 flux with respect to the soil moisture content and temperature. To meet this end, two values within the CO2 flux equation were defined and calculated based on the field measurements; a, representing a water saturation-dependent value, and b, representing the temperature sensitivity (independent of the water saturation). The results show a good agreement between estimated and measured data. Upon which, the maximum baseline for surface CO2 flux was derived and used as a threshold to detect the potential CO2 leakage in the candidate field (INAS, Japan).

Gold Dissolution from Ore with Iodide-Oxidising Bacteria.

Gold leaching from ore using iodide-iodine mixtures is an alternative to gold cyanidation. This study evaluated the ability of iodide-oxidising bacteria to solubilise gold from ore that was mainly composed of gold, pyrite, galena, and chalcopyrite. Eight bacterial strains were successfully isolated from brine. Those strains were incubated in a liquid culture medium containing ore with a gold content of 0.26 wt.% and pulp density of 3.3 w/v% to evaluate their abilities to mediate the dissolution of gold. The gold was solubilised completely within 30 days of incubation in the iodine-iodide lixiviant solution generated by three bacterial strains. One strain, in particular, completed the dissolution of gold within 5 days of incubation and was identified as a member of the genus Roseovarius. Thus, the possibility of bacterial gold leaching using iodide-oxidising bacteria was successfully demonstrated. Bioleaching gold with iodide would likely be more environmentally sustainable than traditional cyanide leaching. Further research is required to evaluate the techno-economic feasibility of this approach.

Petrotoga japonica sp. nov., a thermophilic, fermentative bacterium isolated from Yabase Oilfield in Japan.

A gram-negative, motile, fermentative, thermophilic bacterium, designated AR80(T), was isolated from a high-temperature oil reservoir in Yabase Oilfield in Akita, Japan. Cells were rod-shaped, motile by means of polar flagella, and formed circular, convex, white colonies. The strain grew at 40-65 °C (optimum 60 °C), 0.5-9 % (w/v) NaCl (optimum 0.5-1 %), pH 6-9 (optimum pH 7.5), and elemental sulfur or thiosulfate serves as terminal electron acceptor. Phylogenetic analysis of 16S rRNA gene sequences indicated that strain AR80(T) belonged to the genus Petrotoga and shared approximately 94.5 % sequence similarity with the type species of this genus. The G + C content of genomic DNA was 32.4 mol% while the value of DNA-DNA hybridization between the closest relative species Petrotoga miotherma and AR80(T) was 58.1 %. The major cellular fatty acids of strain AR80(T) consisted of 18:1 w9c, 16:0, and 16:1 w9c. Based on genetic and phenotypic properties, strain AR80(T) was different with other identified Petrotoga species and represents as a novel species, for which the name Petrotoga japonica sp. nov. is proposed. The type strain is AR80(T) (=NBRC 108752(T) = KCTC 15103(T) = HUT 8122(T)).