The benefits of drilling include reducing the total time, maintaining the lowest possible risk, saving costs, and increasing efficiency, which occurs in (the planning and exploration stages). Slow drilling refers to a rate of penetration (ROP) that is not at the desired level. ROP characterizes the speed at which the drill bit penetrates the underlying rock to deepen the borehole, as it is directly related to controlling the speed and efficiency of drilling which ultimately impacts development costs. Predicting ROP is a very important step to optimize drilling with Machine Learning that can assist in solving complex problems with maximum possible efficiency. The model used is PSO-LSSVM treats the penetration drill bit as a continuous process. It takes drilling data sequentially, continuously predicts ROP, and achieves better ROP prediction results. In this case, Hole Depth, weight on bit (WOB), Bit Rotation per minute (RPM), Torque, Bit Depth, Time of Penetration, Hook Load, and Standpipe Pressure, demonstrated influence in keeping ROP at a high level. According to the results, the PSO-LSSVM algorithm can be used for the prediction of ROP in well X. thus providing a solution for prediction and control of operating effects which can result in a fast penetration rate and better efficiency in drilling.

This research is a pre-experimental design (non-design) study which aims to determine how much electricity generation performance of Solar Cell technology is under static load. The independent variable in this study is the intensity of sunlight at the installation location of the Solar Cell technology, while the dependent variable in this study is the performance of the Solar Cell technology, namely voltage (V), current (I), resistance (Ohm) and power. This research uses Solar Cell technology power generation. This research was conducted in Padang Savana, Laikang Village, Mangarabombang District, Takalar Regency, South Sulawesi. The research data were obtained by measuring the input and output of the 200 WP solar cell hybrid technology to determine power and efficiency. To determine the performance of Solar Cell technology against solar radiation, it can be determined by measuring using a lux meter, Avometer, Watt Meter, Tang Ampere. The data analysis technique is descriptive analysis. Based on the results of data analysis, a minimum output power of 12.68 W is obtained with a minimum solar cell efficiency of 4.65% and a maximum output power of 194.2 W with an efficiency of 12.22%. This can be interpreted that the power generation performance of Solar Cell and Wind Turbine technology at static loads is not in accordance with the stated specifications.

Indonesia is a country rich in natural resources. The wealth of Indonesia's natural resources is not only limited to agricultural and plantation products, but also from mineral and hydrocarbon mining or oil and gas. Indonesia's oil production has continued to decline in the last 10 years until the national consumption rate is much higher than national production. One of the causes of the decline in oil production in Indonesia is the condition of Indonesia's oil fields, which are currently mature fields. To overcome the problem of old fields that still have economic oil reserves, enhanced oil recovery (EOR) can be used. One of the EOR methods that can be used is the CO2 injection method using continuous and WAG injection schemes. The study was conducted in X field with oil-wet carbonate rock composition with waterdrive and fluid expansion driving mechanism. The basecase production scheme using 5 production wells produces a recovery factor of 34.3%, while continuous CO2 injection produces a recovery factor of 32%, and CO2-WAG produces a recovery factor of 42%. Continuous CO2 injection has the lowest recovery factor because early gas breaktrough occurs due to a large mobility ratio and causes gas fingering, hindering the oil production process. The most suitable injection method is CO2-WAG 1 cycle using a 1:1 ratio, CO2 injection volume of 6.661 MSCF/D, injection water volume of 37.4 MBBL/D with a recovery factor of 43.46%.

The samples under investigation represent the black shale in interbeds within the Al Aziziyah Formation, located in the NW part of Libya and were analysed using geochemical techniques method and a variety of organic geochemical parameters. The aim of this study is to assess the type of organic matter, thermal maturity, and environment of deposition, based on biomarker distributions and Rock-Eval pyrolysis. The Gas chromatograms of the saturated hydrocarbon fractions of the study samples display a smooth high-end member distribution of the n- n-alkanes extending beyond nC34. The sample displayed evidence of biodegradation as suggested by the presence of the loss of some of the lower molecular weight n-alkanes. The predominance of high molecular weight n-alkane in the study sample suggests a significant input of higher land plant organic matter into these sediments. Geochemical (Rock-Eval, Tmax. values ranging from 416-4390C typical of immature range). Another related feature of these rocks extracted is the presence of a high relative abundance of gammacerane, indicating an anoxic marine hypersaline source depositional environment. The relatively high abundance of common land-plant-derived biomarkers, such as oleananes, is a clear indication of major terrigenous input to the source of these extractable organic matters. The high abundance of C24 tetracyclic terpanes could be associated with higher land-plant, algae, or microbial sources, thereby suggesting a mixed source input. TOC valued < 0.5 wt.%, in the analyzed samples can be considered as a poor source rock generative potential. Based on this study, it can be said that the interbedded shales of the Al Aziziyah Formation seem to have received substantial amounts of land-derived organic matter that has been transported into an open marine subtidal depositional setting had prevailed and was deposited in a photosynthetic-organisms dominated environment (thereby suggesting a mixed source input).

Voltage generation was obtained using a water droplet characterization on a taro (Colocasia esculenta L) leaf surface. This method relies on the superhydrophobic effect from the contact angle between the water droplet and the taro leaf’s surface allowing electron jumping and voltage generation. Water droplets were dropped on the top of taro leaf surface equipped with aluminum foil underneath as an electrode. The voltage was measured at various slope angles of 20°, 40° and 60° in a real-time basis. A digital camera was used to capture the droplet movement and characterization. It is found that the taro leaf has a surface morphology of nano-sized pointed pillars which created a superhydrophobic field. The energy generation was primarily obtained from the electron jump which was caused by the surface tension of the nano-stalagmite structure assisted by the minerals contained in the taro leaf surface. The results reported that the smaller the droplet radius (the smaller the droplet surface area), the greater the droplet surface tension and the greater the voltage generation. Furthermore, the highest voltage generation was obtained 321.2 mV at 20°-degree angle of slopes.

This study aimed to examine the effect of adding KOH, Na2CO3, and Carboxymethyl Cellulose additives on the physical properties of the mud, as well as the optimal additive for sludge production. The preparation of the basal sludge involved the addition of 22.5 grams of bentonite, 350 millilitres of distilled water, and 10 grams of Barite as a constant variable. Then it stated 0.5 variations of the Na2CO3 additive; 1.5; 3 grams, KOH 0.5; 1.5; and 3 grams, and Carboxymethyl Cellulose 3; 6; and 9 grams. A physical property measurement involving density was conducted. Samples were evaluated for Plastic Viscosity and Yield Point at 300 and 600 rpm dial speeds. After 30 minutes of filter press compression, the filtration loss, mud cake, and pH were measured. The results indicate that the KOH additive decreases Yield Point by 8.6 lb/100ft2 and increases Filtrate Loss by 5.8 mL and sediment pH by 11.12 points. The additive Na2CO3 then causes a reduction in Filtrate Loss of 10.4, 8.8, 7.6 mL and an increase in Plastic Viscosity. While Carboxymethyl Cellulose can increase Plastic Viscosity by 7; 13; 55 cP, Gel strength by 4; 6; 40 Lb/100 ft2, and Filtrate Loss by 10; 8; 7.6mL. Carboxymethyl Cellulose is the additive that has the most significant effect on the physical properties of the mud because it can affect Plastic Viscosity, Gel Strength, Yield Point, and Filtrate Loss so that the soil can approach API 13A Standards. The optimal amount of Carboxymethyl Cellulose should be added at a mass of 6 grams, or 13 cP.

The purpose of this study is to determine the condition of electricity supply in Indonesia, which experienced a shortage in 2015, but is currently experiencing an oversupply. Starting from the existence of a 35,000 MW mega project which turned out to be in line with the Covid-19 pandemic, the use of electrical energy was stagnant, even though the addition of electrical energy supply continued to grow. This is also coupled with the problem of the proportion of fossil energy use which is still more than that of new and renewable energy. So that makes PLN have to spend a large amount of money and has not been balanced with the results obtained. The solution is to increase electricity demand, namely by adding new market niches to increase productive electricity demand. As well as through various bundling and promos to increase customer comfort, for example promos to increase power, discount home charging for electric vehicle owners, the use of electric stoves and so on.

In Nigeria, drilling companies import a bulk of drilling fluid materials that they use to carry out their respective operations. This has been a major concern to oil and gas industries since these drilling fluid materials cannot be recycled, are highly expensive in terms of foreign exchange, are not environmentally friendly, not very effective, and non-biodegradable. This work presents an experimental investigation into the reliability of the use of local materials as a substitute to conventional viscosifiers. Local materials used in the analysis are mucuna solannie (Ukpo), brachystegia eurycoma (Achi), and detarium microcarpium (Ofo). The results obtained from the experimental analysis show that they compared closely to the standard viscosifer formulated with Pac-R. The results showed that the density, specific gravity, pH, yield stress, gel strength, plastic viscosity, and yield point of mud formulated from local materials compared favorably with that of the imported viscosifier. An increase in concentration of the local sample also favoured its potential to replace the Pac-R. In terms of cost, cutting carrying ability, and positive environmental impacts, the local materials could serve the Nigerian drilling company better.

In month of August, year 2021, there was an alteration in the production-sharing contract for this field. The contract previously used was Production Sharing Contract (PSC) Cost Recovery, which changed to PSC Gross Split. This contract comparison aims to synergetically evaluate the comparison of the two economic models and also to determine a more efficient and appropriate scheme to be applied to field A, as well as to analyze the parameters that can affect the economic indicators of field A. The results of the economic analysis that has been carried out show that the PSC Gross Split scheme is better than the PSC Cost Recovery scheme. For PSC Cost Recovery, the Net Present Value (NPV) obtained for 30 wells is equal to 13,848,000 US$, the average Interest Rate of Return (IRR) is 118%, the average Pay Out Time (POT) is 1.43 years, the Contractor Take is 20,740,000 US$, and the Government Take is 176,587,000 US$. Whereas for PSC Gross Split, the NPV obtained for 30 wells was US$ 37,906,000, the average IRR was 245%, the average POT was 1.30 years, the Contractor Take was US$ 52,544,000, and the Government Take was 136,402,000 US$. The sensitivity analysis that has been carried out shows that the parameters of the amount of oil production and the price of oil have a significant effect on both schemes.

Artificial intelligence techniques provide an alternative to conventional empirical correlation methods when experimentally determined oil formation volume factors (OFVF) are lacking. A new mathematical model is proposed using an artificial neural network (ANN) for estimating the OFVF for the Niger Delta crude oils. The method consists of two stages: data decorrelation through principal component analysis (PCA) and OFVF estimation through ANN. Data decorrelation was used to reduce redundancy in the data which decreased the number of neurons in the hidden layer needed for an ANN to achieve high accuracy. In the development of the model, 316 data points were obtained from the Niger Delta region of Nigeria. Application of data cleaning, outliers’ elimination and PCA analysis reduced the data to 243 points. 213 data points were used to develop the model of which 75% was used for training, 15% for validation and 10% for testing. The remaining 30 data points were used to test the predictive capability of the proposed model. The results obtained were compared with widely accepted empirical correlations of Standing, Glaso, Vazquez, Ikiensikimama & Ajienka, and Al-Marhoun. The proposed new model performed better than all of them in terms of coefficient of correlation, AAPE and RMSE. Hence the ANN model will reduce cost, save time, and also predict the OFVF of Niger Delta crudes with higher precision.