Pub Date : 2024-01-10DOI: 10.25299/jeee.2023.14004
Wilma Latuny
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.
{"title":"APPLICATION OF PSO-LSSVM IN PREDICTION AND ANALYSIS OF SLOW DRILLING (RATE OF PENETRATION)","authors":"Wilma Latuny","doi":"10.25299/jeee.2023.14004","DOIUrl":"https://doi.org/10.25299/jeee.2023.14004","url":null,"abstract":"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.","PeriodicalId":33635,"journal":{"name":"Journal of Earth Energy Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139534553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-10DOI: 10.25299/jeee.2023.13689
Pijar Fitrah Ababil, Hadziqul Abror, Riska Laksmita Sari
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%.
{"title":"EVALUATION OF CONTINUOUS AND WATER ALTERNATING GAS (WAG) CO2 INJECTION ON X FIELD RECOVERY FACTOR","authors":"Pijar Fitrah Ababil, Hadziqul Abror, Riska Laksmita Sari","doi":"10.25299/jeee.2023.13689","DOIUrl":"https://doi.org/10.25299/jeee.2023.13689","url":null,"abstract":"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%.","PeriodicalId":33635,"journal":{"name":"Journal of Earth Energy Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139627985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"Oil Formation Volume Factor Prediction Using Artificial Neural Network: A Case Study of Niger Delta Crudes","authors":"Chiebuka Okoro, Angela Nwachukwu","doi":"10.25299/jeee.2022.7121","DOIUrl":"https://doi.org/10.25299/jeee.2022.7121","url":null,"abstract":"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.","PeriodicalId":33635,"journal":{"name":"Journal of Earth Energy Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135930379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"Characterization of Voltage Generation Obtained from Water Droplets on a Taro Leaf (Colocasia esculenta L) Surface","authors":"Ena Marlina, Akhmad Faruq Alhikami, Metty Trisna Negara, Sekar Rahima Sahwahita, Mochammad Basjir","doi":"10.25299/jeee.2023.12916","DOIUrl":"https://doi.org/10.25299/jeee.2023.12916","url":null,"abstract":"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.","PeriodicalId":33635,"journal":{"name":"Journal of Earth Energy Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135930386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sayen Girsang, None Deny Fatryanto, None Rohima Sera Afifah
CO2 injection is one of the Enhanced Oil Recovery (EOR) methods. In this study Water alternating gas (WAG) CO2 injection method was used to obain the maximum sweep efficiency. The purpose of this study was to analyze the effect of gas water ratio (GWR) value on recovery and CO2 storage capacity, and to analyze the best scenario in term of technical objective.
This study was carried out using E300 reservoir simulator. The increase in recovery and CO2 storage were observed throught the parameters of recovery factor and CO2 storage capacity, while the determination of the best scenario in term of technical objective was observed using the parameters of objective function. This study was carried out in 3 different scenarios, which were the injection of 100% CO2, 60% CO2and 40% water, and 40% CO2 and 60% water
Based on the observation, it was founded that third scenario with the GWR of 40:60 resulted the highest cumulative production and recovery factor with the value reaching 14.1 milliom m3 and 67.4%. Meanwhile the second scenario with the GWR of 60:40 has the highest CO2 storage capacity of 3 billion Sm3 CO2. The second scenario has the best performance in term of technical objective with the value of objective function reaching 0.45.
注二氧化碳是提高采收率(EOR)的方法之一。本研究采用水交替气(WAG) CO2注入法获得最大波及效率。本研究的目的是分析气水比(GWR)值对采收率和CO2储存量的影响，并根据技术目标分析最佳方案。本研究采用E300油藏模拟器进行。通过采收率和CO2储存量参数来观察采收率和CO2储存量的增加，通过目标函数参数来确定技术目标的最佳方案。本研究采用100% CO2、60% CO2 + 40%水、40% CO2 + 60%水3种不同的方案进行研究;通过观察发现，GWR为40:60的情景下，累计产量和采收率最高，达到1410万m3，累计采收率为67.4%。GWR为60:40的情景下，CO2储存量最高，达到30亿Sm3 CO2。第二种方案在技术目标方面表现最好，目标函数值达到0.45。
{"title":"The Effect of Different Gas Water Ratio on Recovery Factor and CO2 Storage Capacity in Water Alternating Gas Injection. A Case Study: “V” Field Development, North Sea","authors":"Sayen Girsang, None Deny Fatryanto, None Rohima Sera Afifah","doi":"10.25299/jeee.2022.6097","DOIUrl":"https://doi.org/10.25299/jeee.2022.6097","url":null,"abstract":"CO2 injection is one of the Enhanced Oil Recovery (EOR) methods. In this study Water alternating gas (WAG) CO2 injection method was used to obain the maximum sweep efficiency. The purpose of this study was to analyze the effect of gas water ratio (GWR) value on recovery and CO2 storage capacity, and to analyze the best scenario in term of technical objective.
 This study was carried out using E300 reservoir simulator. The increase in recovery and CO2 storage were observed throught the parameters of recovery factor and CO2 storage capacity, while the determination of the best scenario in term of technical objective was observed using the parameters of objective function. This study was carried out in 3 different scenarios, which were the injection of 100% CO2, 60% CO2and 40% water, and 40% CO2 and 60% water
 Based on the observation, it was founded that third scenario with the GWR of 40:60 resulted the highest cumulative production and recovery factor with the value reaching 14.1 milliom m3 and 67.4%. Meanwhile the second scenario with the GWR of 60:40 has the highest CO2 storage capacity of 3 billion Sm3 CO2. The second scenario has the best performance in term of technical objective with the value of objective function reaching 0.45.","PeriodicalId":33635,"journal":{"name":"Journal of Earth Energy Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135930252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-31DOI: 10.25299/jeee.2023.12856
Appin Purisky Redaputri
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.
{"title":"The Condition of Excess Electricity Supply in Indonesia","authors":"Appin Purisky Redaputri","doi":"10.25299/jeee.2023.12856","DOIUrl":"https://doi.org/10.25299/jeee.2023.12856","url":null,"abstract":"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.","PeriodicalId":33635,"journal":{"name":"Journal of Earth Energy Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135931965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Boni Swadesi, Ahmad Azhar Ilyas, Maria Theresia Kristiati, Dewi Asmorowati, Ahmad Sobri, Sukma Bayu, Malvin Larasyad Azwar
The design of the fracturing fluid is a very important aspect of the success of hydraulic fracturing. The most common fracturing fluid used in hydraulic fracturing is the cross-linked guar gum fracturing fluid. To determine the optimal fracturing fluid concentration, it is necessary to analyze the fracturing fluid optimization to obtain the best fracturing results in terms of fracturing fluid rheology, regain permeability, hydraulics, cost, fracture geometry, and FOI. From this analysis, it is expected to obtain the most optimal fracturing fluid to be applied to the JARWO Well. This research was conducted by conducting a sensitivity test method for selecting the concentration of the fracturing fluid system that affects the fracture fluid rheology, regain permeability, fracturing fluid hydraulics during injection, total material cost, fracture geometry, and the resulting FOI. The sensitivity of the fracturing fluid concentration that was tested was the system concentration of 35 pptg, 40 pptg, and 45 pptg. Each fracturing fluid is tested in the laboratory to obtain rheology which will then be simulated using MFrac software to obtain the fracture geometry formed. The results of the analysis of the concentration of each fracturing fluid showed that the fracturing fluid with a system concentration of 40 pptg was the most stable in viscosity at pumping time to produce the highest FOI. The hydraulic fracturing fluid with a concentration of 40 pptg is better than that of a concentration of 45 pptg. From the performance of regaining permeability and residue, it is quite good when compared to fracturing fluid with concentration of 45 pptg, and the cost is lower when compared to a fracturing fluid with concentration of 45 pptg. So that the fracturing fluid with a system concentration of 40 pptg is the most optimal fluid for use in hydraulic fracturing activities at the JARWO Well.
{"title":"Fracturing Fluid Optimization in Limestone Formation Using Guar Gum Crosslinked Fluid","authors":"Boni Swadesi, Ahmad Azhar Ilyas, Maria Theresia Kristiati, Dewi Asmorowati, Ahmad Sobri, Sukma Bayu, Malvin Larasyad Azwar","doi":"10.25299/jeee.2023.8026","DOIUrl":"https://doi.org/10.25299/jeee.2023.8026","url":null,"abstract":"The design of the fracturing fluid is a very important aspect of the success of hydraulic fracturing. The most common fracturing fluid used in hydraulic fracturing is the cross-linked guar gum fracturing fluid. To determine the optimal fracturing fluid concentration, it is necessary to analyze the fracturing fluid optimization to obtain the best fracturing results in terms of fracturing fluid rheology, regain permeability, hydraulics, cost, fracture geometry, and FOI. From this analysis, it is expected to obtain the most optimal fracturing fluid to be applied to the JARWO Well. This research was conducted by conducting a sensitivity test method for selecting the concentration of the fracturing fluid system that affects the fracture fluid rheology, regain permeability, fracturing fluid hydraulics during injection, total material cost, fracture geometry, and the resulting FOI. The sensitivity of the fracturing fluid concentration that was tested was the system concentration of 35 pptg, 40 pptg, and 45 pptg. Each fracturing fluid is tested in the laboratory to obtain rheology which will then be simulated using MFrac software to obtain the fracture geometry formed. The results of the analysis of the concentration of each fracturing fluid showed that the fracturing fluid with a system concentration of 40 pptg was the most stable in viscosity at pumping time to produce the highest FOI. The hydraulic fracturing fluid with a concentration of 40 pptg is better than that of a concentration of 45 pptg. From the performance of regaining permeability and residue, it is quite good when compared to fracturing fluid with concentration of 45 pptg, and the cost is lower when compared to a fracturing fluid with concentration of 45 pptg. So that the fracturing fluid with a system concentration of 40 pptg is the most optimal fluid for use in hydraulic fracturing activities at the JARWO Well.","PeriodicalId":33635,"journal":{"name":"Journal of Earth Energy Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135930380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-31DOI: 10.25299/jeee.2023.13497
Adi Ilcham, Muhammad Adittyanto, Khairul Asrori, Wakhid Umar
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.
{"title":"Bentonite-Based Drilling Boyolali Mud Fabrication with Additive Carboxymethyl Cellulose, Na2CO3 and KOH","authors":"Adi Ilcham, Muhammad Adittyanto, Khairul Asrori, Wakhid Umar","doi":"10.25299/jeee.2023.13497","DOIUrl":"https://doi.org/10.25299/jeee.2023.13497","url":null,"abstract":"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.","PeriodicalId":33635,"journal":{"name":"Journal of Earth Energy Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135931962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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$. The sensitivity analysis that has been carried out shows that the parameters of the amount of oil production and the price of oil have a significant effect on both schemes.
{"title":"The Synergetic Economic Evaluation of PSC Cost Recovery and Gross Split Schemes on Field A","authors":"Prayang Sunny Yulia, Adji Nadzif Sidqi, Syamsul Irham, Mustamina Maulani, Puri Wijayanti","doi":"10.25299/jeee.2023.12530","DOIUrl":"https://doi.org/10.25299/jeee.2023.12530","url":null,"abstract":"In month of August, year 2021, there was an alteration in the production-sharing contract for this field. The contract previously used was Production Sharing Contract (PSC) Cost Recovery, which changed to PSC Gross Split. This contract comparison aims to synergetically evaluate the comparison of the two economic models and also to determine a more efficient and appropriate scheme to be applied to field A, as well as to analyze the parameters that can affect the economic indicators of field A. The results of the economic analysis that has been carried out show that the PSC Gross Split scheme is better than the PSC Cost Recovery scheme. For PSC Cost Recovery, the Net Present Value (NPV) obtained for 30 wells is equal to 13,848,000 US$, 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.","PeriodicalId":33635,"journal":{"name":"Journal of Earth Energy Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135936158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-09DOI: 10.25299/jeee.2023.13955
Zikra Miftahul Haq, Eki Komara, W. Lestari
This research was conducted using EEI inversion on seismic data in Z Field, Kutai Basin. The EEI inversion is effectively used to determine the reservoir distribution by eliminating the angle limit on the elastic impedance to the Chi angle so that it can be correlated with petrophysical parameters that are sensitive to lithology and fluids. The data used in this study are well data, checkshots, horizons, and partial-stack angle gather 3D seismic data. The data obtained is processed to obtain the target zone first based on log interpretation. Based on data processing, the target zone is obtained at 1513 m to 1531 m. Sensitivity analysis was conducted to determine the sensitive parameters, which can separate the lithology of the formation. In the sensitivity analysis, the most sensitive log to separate lithology is the Vp/VS log, which can separate sandstone, shale, and coal. Furthermore, the EEI inversion analysis was carried out to obtain the most suiTable model for the inversion, the Based Hard Constraint model was obtained with a correlation reaching 0.997 and an error value of 0.078. Based on the EEI inversion, the target zone in the Z-field at a depth of 1258 ms - 1269 ms with a sandstone reservoir in the EEI range of 6000 (m/s)(g/cc) - 7500 (m/s)(g/cc) which spreads from northeast to south. The distribution of the sandstone reservoir is surrounded by coal with a range of EEI 7500 (m/s)(g/cc) - 12000 (m/s)(g/cc), and also the distribution of shale in the EEI range of 7500(m/s)( g/cc) - 9200(m/s)(g/cc).
{"title":"Identification of Reservoir Distribution Using Extended Elastic Impedance (EEI) Inversion in the \"Z\" Field of the Kutai Basin","authors":"Zikra Miftahul Haq, Eki Komara, W. Lestari","doi":"10.25299/jeee.2023.13955","DOIUrl":"https://doi.org/10.25299/jeee.2023.13955","url":null,"abstract":"This research was conducted using EEI inversion on seismic data in Z Field, Kutai Basin. The EEI inversion is effectively used to determine the reservoir distribution by eliminating the angle limit on the elastic impedance to the Chi angle so that it can be correlated with petrophysical parameters that are sensitive to lithology and fluids. The data used in this study are well data, checkshots, horizons, and partial-stack angle gather 3D seismic data. The data obtained is processed to obtain the target zone first based on log interpretation. Based on data processing, the target zone is obtained at 1513 m to 1531 m. Sensitivity analysis was conducted to determine the sensitive parameters, which can separate the lithology of the formation. In the sensitivity analysis, the most sensitive log to separate lithology is the Vp/VS log, which can separate sandstone, shale, and coal. Furthermore, the EEI inversion analysis was carried out to obtain the most suiTable model for the inversion, the Based Hard Constraint model was obtained with a correlation reaching 0.997 and an error value of 0.078. Based on the EEI inversion, the target zone in the Z-field at a depth of 1258 ms - 1269 ms with a sandstone reservoir in the EEI range of 6000 (m/s)(g/cc) - 7500 (m/s)(g/cc) which spreads from northeast to south. The distribution of the sandstone reservoir is surrounded by coal with a range of EEI 7500 (m/s)(g/cc) - 12000 (m/s)(g/cc), and also the distribution of shale in the EEI range of 7500(m/s)( g/cc) - 9200(m/s)(g/cc).","PeriodicalId":33635,"journal":{"name":"Journal of Earth Energy Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41907755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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