Y. Baba, A. Aliyu, N. E. Okeke, A. S. Girei, H. Yeung
Slug translational velocity, described as the velocity of slug units, is the summation of the maximum mixture velocity in the slug body and the drift velocity. Accurate estimation of this parameter is important for energy-efficient design of oil and gas pipelines. A survey of the literature revealed that existing prediction models of this parameter were developed based on observation from low viscosity liquids (of 1 Pa.s or less). However, its behaviour in pipes transporting higher viscosity oils is significantly different. In this research work, new data for slug translational velocity in high-viscosity oil-gas flows are reported. Scaled experiments were carried out using a mixture of air and Mineral oil of viscosity ranging from 0.7 to 6.0 Pa.s in a 17-m long horizontal pipe of 0.0762 m ID. Temperature dependence of the oil's viscosity is given as μ=−0.0043T3+0.0389T2−1.4174T+18.141. The slug translational velocity was measured by means two pairs of two fast-sampling Gamma Densitometers with a sampling frequency of 250 Hz. For the range of experimental flow conditions investigated, increase in liquid oil viscosity was observed to strongly influence slug translational velocity. A new predictive correlation incorporating the effect of viscosity on slug translational velocity was derived using the current dataset and incorporating those obtained in literature with oil viscosity ranging from 0.189–6.0 Pa.s for horizontal flow. A comparison by statistical analysis and validation and of the new closure relationship showed a remarkably improved performance over existing correlations.
{"title":"Evaluating the Effects of High Viscosity Liquid on Two Phase Flow Slug Translational Velocity using Gamma Radiation Methods","authors":"Y. Baba, A. Aliyu, N. E. Okeke, A. S. Girei, H. Yeung","doi":"10.2118/198720-MS","DOIUrl":"https://doi.org/10.2118/198720-MS","url":null,"abstract":"\u0000 Slug translational velocity, described as the velocity of slug units, is the summation of the maximum mixture velocity in the slug body and the drift velocity. Accurate estimation of this parameter is important for energy-efficient design of oil and gas pipelines. A survey of the literature revealed that existing prediction models of this parameter were developed based on observation from low viscosity liquids (of 1 Pa.s or less). However, its behaviour in pipes transporting higher viscosity oils is significantly different. In this research work, new data for slug translational velocity in high-viscosity oil-gas flows are reported. Scaled experiments were carried out using a mixture of air and Mineral oil of viscosity ranging from 0.7 to 6.0 Pa.s in a 17-m long horizontal pipe of 0.0762 m ID. Temperature dependence of the oil's viscosity is given as μ=−0.0043T3+0.0389T2−1.4174T+18.141. The slug translational velocity was measured by means two pairs of two fast-sampling Gamma Densitometers with a sampling frequency of 250 Hz. For the range of experimental flow conditions investigated, increase in liquid oil viscosity was observed to strongly influence slug translational velocity. A new predictive correlation incorporating the effect of viscosity on slug translational velocity was derived using the current dataset and incorporating those obtained in literature with oil viscosity ranging from 0.189–6.0 Pa.s for horizontal flow. A comparison by statistical analysis and validation and of the new closure relationship showed a remarkably improved performance over existing correlations.","PeriodicalId":11110,"journal":{"name":"Day 2 Tue, August 06, 2019","volume":"161 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75381908","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}
J. Dala, Lateef T. Akanji, Olafuyi Olalekan, K. Bello, Prashant Jadhawar
The impact of production tubing diameter on multiphase flow regime profile is investigated. For a given isolated section of the production tubing in selected wells drilled and completed in Oredo fields, velocity profile and fluid flow characteristics at the production tubing centreline and along the pipe wall were evaluated. Complex flow behaviour is characterised by tubing diameter and asymptotic flow pattern at the tubing surface where no-slip boundary condition was imposed. Future inflow production performance relationship (IPR) and influence on vertical lift performance are determinable from the multiphase flow regime profiles. Furthermore, we investigate the impact of production tubing diameter on multiphase flow regime profile in Oredo oil field Nigeria. Mechanisms responsible for complex fluid flow behaviour and transition in different tubing configuration with implication on production optimisation and performance analysis are also included in the model design and analysis. From the results obtained in this study, it is evident that higher volume of oil is producible from bigger production tubing. However, implications on vertical lift performance and production optimisation require a more critical analyses.
{"title":"Impact of Well Production Tubing Diameter on Multiphase Flow Regime Profile in Oredo Fields, Niger Delta, Nigeria","authors":"J. Dala, Lateef T. Akanji, Olafuyi Olalekan, K. Bello, Prashant Jadhawar","doi":"10.2118/198876-MS","DOIUrl":"https://doi.org/10.2118/198876-MS","url":null,"abstract":"\u0000 The impact of production tubing diameter on multiphase flow regime profile is investigated. For a given isolated section of the production tubing in selected wells drilled and completed in Oredo fields, velocity profile and fluid flow characteristics at the production tubing centreline and along the pipe wall were evaluated. Complex flow behaviour is characterised by tubing diameter and asymptotic flow pattern at the tubing surface where no-slip boundary condition was imposed. Future inflow production performance relationship (IPR) and influence on vertical lift performance are determinable from the multiphase flow regime profiles. Furthermore, we investigate the impact of production tubing diameter on multiphase flow regime profile in Oredo oil field Nigeria. Mechanisms responsible for complex fluid flow behaviour and transition in different tubing configuration with implication on production optimisation and performance analysis are also included in the model design and analysis. From the results obtained in this study, it is evident that higher volume of oil is producible from bigger production tubing. However, implications on vertical lift performance and production optimisation require a more critical analyses.","PeriodicalId":11110,"journal":{"name":"Day 2 Tue, August 06, 2019","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72740910","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}
A. Jaja, Nnamdi Okani, Vincent Eme, Ricardo Combella Ricardos, Chevron Gom, Lynn Silpngarmlers
In the Early phases of field development, the drilled hydrocarbon appraisal wells may not have been sufficient to define rock properties, fluid typing and contacts. It's very important to define the range of uncertainty in such fields. This is because as the field matures other dynamic data will become available to validate these probable volumes. The ideal development scenario provides the practitioner with a full suite of data defining the reservoir geometries, reservoir properties, fluid properties etc. to make subsurface decisions. However, in most cases, operational realities will deny the reservoir practitioner this full suite of data. One practical convention that is used to resolve this data paucity challenge is to evaluate and report the lowest possible volume, if this low case is economic the project will be economic with potential for more upside outcomes. However, a challenge that can arise with this is that after several iterations the low case can become the only case. A better practice is to characterize uncertainty of reservoir parameters during the early stages of field development and carry out the full range outcomes through the field's life. These ranges will then be validated as the field matures. This paper demonstrates a case in the Niger Delta field A05 reservoir were dynamic simulation model was used to narrow the uncertainty range on the GOC. Proper identification and characterization of the GOC uncertainties helped for the estimate of a range of STOOIP used for dynamic simulation model. Though no static dataset was available to reduce this uncertainty on the GOC, during dynamic simulation, the high-case oil in-place volume was found to be the best match to historical production data with the integration of another reservoir, Delta A12, in one dynamic simulation model. Both reservoirs communicate through the aquifer, separated by a saddle. This then proved up additional volumes in the reservoir, identified previously overlooked reserves and allowed the asset team to propose an extra infill well opportunity than what was previously planned. This new understanding of the A05 reservoir increased the oil estimated ultimate recovery (EUR) by 4.6 MMSTBO.
{"title":"Creating Value Through Integrated Reservoir Study in Mature Asset via Reservoir Uncertainty Characterization: A Case Study from the Niger Delta Field A05 Reservoir","authors":"A. Jaja, Nnamdi Okani, Vincent Eme, Ricardo Combella Ricardos, Chevron Gom, Lynn Silpngarmlers","doi":"10.2118/198822-MS","DOIUrl":"https://doi.org/10.2118/198822-MS","url":null,"abstract":"\u0000 In the Early phases of field development, the drilled hydrocarbon appraisal wells may not have been sufficient to define rock properties, fluid typing and contacts. It's very important to define the range of uncertainty in such fields. This is because as the field matures other dynamic data will become available to validate these probable volumes.\u0000 The ideal development scenario provides the practitioner with a full suite of data defining the reservoir geometries, reservoir properties, fluid properties etc. to make subsurface decisions. However, in most cases, operational realities will deny the reservoir practitioner this full suite of data.\u0000 One practical convention that is used to resolve this data paucity challenge is to evaluate and report the lowest possible volume, if this low case is economic the project will be economic with potential for more upside outcomes. However, a challenge that can arise with this is that after several iterations the low case can become the only case. A better practice is to characterize uncertainty of reservoir parameters during the early stages of field development and carry out the full range outcomes through the field's life. These ranges will then be validated as the field matures.\u0000 This paper demonstrates a case in the Niger Delta field A05 reservoir were dynamic simulation model was used to narrow the uncertainty range on the GOC. Proper identification and characterization of the GOC uncertainties helped for the estimate of a range of STOOIP used for dynamic simulation model. Though no static dataset was available to reduce this uncertainty on the GOC, during dynamic simulation, the high-case oil in-place volume was found to be the best match to historical production data with the integration of another reservoir, Delta A12, in one dynamic simulation model. Both reservoirs communicate through the aquifer, separated by a saddle. This then proved up additional volumes in the reservoir, identified previously overlooked reserves and allowed the asset team to propose an extra infill well opportunity than what was previously planned. This new understanding of the A05 reservoir increased the oil estimated ultimate recovery (EUR) by 4.6 MMSTBO.","PeriodicalId":11110,"journal":{"name":"Day 2 Tue, August 06, 2019","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75051346","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}
Reservoir performance analysis and forecasting poses a challenge in brown fields with limited Bottom Hole Pressure (BHP) data. Most brown Oil Fields in Nigeria have the problem of limited BHP data. Most fields from 1960 to 1970 are having the problem because they were not properly managed from inception as per their data storage and recording systems. In Nigeria, many farm-out assets to the Marginal Operators from the International Oil Companies are having such challenges and managing such assets is daunting and time-consuming because of limited BHP data. Reservoir management is central to the effective exploitation of any hydrocarbon asset and is heightened for the development of brown fields. The problem of limited BHP data also makes reservoir pressure history matching difficult. This study proposes a workflow implemented on an Excel VBA program for a brown field in Niger Delta region of Nigeria with limited pressure (BHP) data. The program, which was validated, was used to history match the observed field production data, the limited reservoir BHP data, predict reservoir performance and production profile within the specified timeframe. The tool is built on Material Balance Equation which is a zero-dimensional-tank model created for the reservoir. Pressure Volume Temperature (PVT) properties with correlations, fractional flow models and Corey correlation for relative permeability estimates were incorporated in the algorithm. The matched model calculates the likely parameters of the reservoir and aquifer, then the production history calibrates the pressure model. The following approach is used in resolving the problem for the field case study. Production data aggregation; Calibration of PVT laboratory data; Calibration of reservoir pressure model; Development of fractional flow model for the system; Calibration of the reservoir's last production date. This approach gives an accurate reservoir pressure history prediction, and also good production forecast in field with limited BHP data.
{"title":"Solution to Limited Pressure BHP Data in Brown Fields; Material Balance Equation Approach","authors":"Bright Agbodike, U. Osokogwu, G. Achumba","doi":"10.2118/198785-MS","DOIUrl":"https://doi.org/10.2118/198785-MS","url":null,"abstract":"\u0000 Reservoir performance analysis and forecasting poses a challenge in brown fields with limited Bottom Hole Pressure (BHP) data. Most brown Oil Fields in Nigeria have the problem of limited BHP data. Most fields from 1960 to 1970 are having the problem because they were not properly managed from inception as per their data storage and recording systems. In Nigeria, many farm-out assets to the Marginal Operators from the International Oil Companies are having such challenges and managing such assets is daunting and time-consuming because of limited BHP data. Reservoir management is central to the effective exploitation of any hydrocarbon asset and is heightened for the development of brown fields. The problem of limited BHP data also makes reservoir pressure history matching difficult. This study proposes a workflow implemented on an Excel VBA program for a brown field in Niger Delta region of Nigeria with limited pressure (BHP) data. The program, which was validated, was used to history match the observed field production data, the limited reservoir BHP data, predict reservoir performance and production profile within the specified timeframe. The tool is built on Material Balance Equation which is a zero-dimensional-tank model created for the reservoir. Pressure Volume Temperature (PVT) properties with correlations, fractional flow models and Corey correlation for relative permeability estimates were incorporated in the algorithm. The matched model calculates the likely parameters of the reservoir and aquifer, then the production history calibrates the pressure model. The following approach is used in resolving the problem for the field case study. Production data aggregation; Calibration of PVT laboratory data; Calibration of reservoir pressure model; Development of fractional flow model for the system; Calibration of the reservoir's last production date.\u0000 This approach gives an accurate reservoir pressure history prediction, and also good production forecast in field with limited BHP data.","PeriodicalId":11110,"journal":{"name":"Day 2 Tue, August 06, 2019","volume":"89 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74960943","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}
Tertiary oil recovery techniques comprise miscible flooding, chemical, thermal and microbial injection into oil reservoirs to enhanced recovery. Several studies have been performed on the use of surfactants and polymers injection for enhanced oil recovery. But this study is focused on isolation and characterization of hydrocarbon utilizing bacteria for biosurfactants and biopolymers production. The concept of microbial enhanced oil recovery consists the injection of nutrients to activate indigenous microbes in the reservoir or injection of external hydrocarbon degrading microbes plus nutrients during field applications to ensure the organisms produce the required metabolites. These microbes have the ability to produce gases to increase reservoir pressure and displacement of immobile oil, bio-surfactants to reduce interfacial tension, biopolymer for mobility control, Injectivity profile and viscosity modification, solvent, acid and biomass. In this study, soil samples were obtained from hydrocarbon-contaminated site in Gio, Tai Local Government Area, in Ogoniland, Rivers State, Nigeria. The samples were transferred into a polythene bag, placed in an ice pack, and transported immediately to the laboratory for physicochemical and microbiological analyses such as emulsification index, haemolytic activity and oil spreading technique. 37 isolates were tested for biosurfactant production and 3 of the isolates were selected for biosurfactant production with strong ability to degrade hydrocarbon. The selected microbes (Bacillus sp, Pseudomonas sp and Enterobacter sp) were identified by biochemical characterization and subjected to ranges of temperature, pH, nutrient sources, salinity, and inoculum concentration to determine their optimum reservoir performance conditions. The result shows the optimum parameter ranges for the three microbes: pH 7-8, temperature within 25 – 35°C, salinity within 0.5% - 5%, the result shows that as the inoculum size increases, the more the emulsification index, the best nitrogen source is peptone and the best carbon source for bacillus sp is glucose and glycerol for Pseudomonas sp and Enterobacter sp.
{"title":"Isolation and Screening of Hydrocarbon Utilizing Bacteria for Biosurfactant Production: Application for Enhanced Oil Recovery","authors":"O. Sylvester, M. Onyekonwu, G. Okpokwasili","doi":"10.2118/198784-MS","DOIUrl":"https://doi.org/10.2118/198784-MS","url":null,"abstract":"\u0000 Tertiary oil recovery techniques comprise miscible flooding, chemical, thermal and microbial injection into oil reservoirs to enhanced recovery. Several studies have been performed on the use of surfactants and polymers injection for enhanced oil recovery. But this study is focused on isolation and characterization of hydrocarbon utilizing bacteria for biosurfactants and biopolymers production. The concept of microbial enhanced oil recovery consists the injection of nutrients to activate indigenous microbes in the reservoir or injection of external hydrocarbon degrading microbes plus nutrients during field applications to ensure the organisms produce the required metabolites. These microbes have the ability to produce gases to increase reservoir pressure and displacement of immobile oil, bio-surfactants to reduce interfacial tension, biopolymer for mobility control, Injectivity profile and viscosity modification, solvent, acid and biomass. In this study, soil samples were obtained from hydrocarbon-contaminated site in Gio, Tai Local Government Area, in Ogoniland, Rivers State, Nigeria. The samples were transferred into a polythene bag, placed in an ice pack, and transported immediately to the laboratory for physicochemical and microbiological analyses such as emulsification index, haemolytic activity and oil spreading technique. 37 isolates were tested for biosurfactant production and 3 of the isolates were selected for biosurfactant production with strong ability to degrade hydrocarbon. The selected microbes (Bacillus sp, Pseudomonas sp and Enterobacter sp) were identified by biochemical characterization and subjected to ranges of temperature, pH, nutrient sources, salinity, and inoculum concentration to determine their optimum reservoir performance conditions. The result shows the optimum parameter ranges for the three microbes: pH 7-8, temperature within 25 – 35°C, salinity within 0.5% - 5%, the result shows that as the inoculum size increases, the more the emulsification index, the best nitrogen source is peptone and the best carbon source for bacillus sp is glucose and glycerol for Pseudomonas sp and Enterobacter sp.","PeriodicalId":11110,"journal":{"name":"Day 2 Tue, August 06, 2019","volume":"79 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84835978","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}
Detection of small leaks in gas pipelines is an important and persistent problem in the oil and gas industry. However, the industry is beginning to investigate how tools of Machine Learning, Artificial Intelligence, Big Data, etc. can be used to improve current industry processes. This work aims to study the ability of intelligent models to detect small leaks in a natural gas pipeline using operational parameters such as pressure, temperature and flowrate through existing industry performance metrics (sensitivity, reliability, robustness and accuracy). Observer design technique was applied to detect leaks in a gas pipeline using a regresso-classification hierarchical model where an intelligent model acts as a regressor and a leak detection algorithm acts as a classifier. Five intelligent models (Gradient Boosting, Decision Trees, Random Forest, Support Vector Machine and Artificial Neural Network) were used in this present work. Results showed that the Random Forest and Decision Tree models are the most sensitive as they can detect a leak of 0.1% of nominal flow in about 2 hours. All the intelligent models had high reliability with zero false alarm rate in testing phase. However, due to this level of reliability, the models had low accuracy with the Artificial Neural Network and Support Vector Machine performing best and better regressors than the others. All the intelligent models are robust. The average time to leak detection for different leak sizes for all the intelligent models were compared to a real time transient model in literature. The intelligent models had a time savings of 25% to 48%. Results in this present work further suggest that intelligent models could be used alongside a real time transient model to improve leak detection. Also, that the tools of big data, data analytics, artificial intelligence can be harnessed to improving leak detection results.
{"title":"Leak Detection in Natural Gas Pipelines Using Intelligent Models","authors":"O. Akinsete, Adebayo Oshingbesan","doi":"10.2118/198738-MS","DOIUrl":"https://doi.org/10.2118/198738-MS","url":null,"abstract":"\u0000 Detection of small leaks in gas pipelines is an important and persistent problem in the oil and gas industry. However, the industry is beginning to investigate how tools of Machine Learning, Artificial Intelligence, Big Data, etc. can be used to improve current industry processes.\u0000 This work aims to study the ability of intelligent models to detect small leaks in a natural gas pipeline using operational parameters such as pressure, temperature and flowrate through existing industry performance metrics (sensitivity, reliability, robustness and accuracy). Observer design technique was applied to detect leaks in a gas pipeline using a regresso-classification hierarchical model where an intelligent model acts as a regressor and a leak detection algorithm acts as a classifier. Five intelligent models (Gradient Boosting, Decision Trees, Random Forest, Support Vector Machine and Artificial Neural Network) were used in this present work.\u0000 Results showed that the Random Forest and Decision Tree models are the most sensitive as they can detect a leak of 0.1% of nominal flow in about 2 hours. All the intelligent models had high reliability with zero false alarm rate in testing phase. However, due to this level of reliability, the models had low accuracy with the Artificial Neural Network and Support Vector Machine performing best and better regressors than the others. All the intelligent models are robust. The average time to leak detection for different leak sizes for all the intelligent models were compared to a real time transient model in literature. The intelligent models had a time savings of 25% to 48%.\u0000 Results in this present work further suggest that intelligent models could be used alongside a real time transient model to improve leak detection. Also, that the tools of big data, data analytics, artificial intelligence can be harnessed to improving leak detection results.","PeriodicalId":11110,"journal":{"name":"Day 2 Tue, August 06, 2019","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85893074","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}
Without doubt, one of the most frequently occurring problems in production facilities with dire consequences is sand production. Sand production refers to the continuous flow of formation grains alongside reservoir fluids during production. It is a problem that is more associated with unconsolidated reservoirs such as those present in the Niger Delta. Some of the problems associated with sand production include stabilization of emulsion, vessel blockage, erosion of vessels and reduction in separation effectiveness (Bibobra et al, 2015) all of which have economic consequences, thus, can render companies bankrupt. In a bid to avoid the aforementioned problems, many sand control measures have been developed, however, with increase in effectiveness in handling sand comes a corresponding increase in cost, hence, necessitating a feasibility study to ascertain their viability. Many authors have developed mathematical models useful in predicting sand production in reservoirs. These models have proved to be useful tools in sand control viability studies. Geomechanical parameters like principal stresses have been useful in these models. However, many of these models developed have turned out complex, requiring difficult-to-obtain parameters or having low level of accuracy when compared to observed field data. In this paper, a mathematical model was developed by modifying the work of Oluyemi and Oyeneyin (2010) who developed a simple mechanistic model requiring few and easy-to-obtain input parameters. A simple simulator, named Cassandra, was then designed using Python Programming Language so as to aid the estimation process. After validation with field data from different reservoirs, it was found that Cassandra gave results very close to observed field data, in fact, it only possesses about 8% absolute error. The model also performed excellently when compared to existing models. This software, thus, proves to be a valuable tool in any sand production analysis.
{"title":"Cassandra: A Model and Simulator Developed for Critical Drawdown Estimation in Unconsolidated Reservoirs","authors":"Precious Ehihamen","doi":"10.2118/198803-MS","DOIUrl":"https://doi.org/10.2118/198803-MS","url":null,"abstract":"Without doubt, one of the most frequently occurring problems in production facilities with dire consequences is sand production. Sand production refers to the continuous flow of formation grains alongside reservoir fluids during production. It is a problem that is more associated with unconsolidated reservoirs such as those present in the Niger Delta. Some of the problems associated with sand production include stabilization of emulsion, vessel blockage, erosion of vessels and reduction in separation effectiveness (Bibobra et al, 2015) all of which have economic consequences, thus, can render companies bankrupt.\u0000 In a bid to avoid the aforementioned problems, many sand control measures have been developed, however, with increase in effectiveness in handling sand comes a corresponding increase in cost, hence, necessitating a feasibility study to ascertain their viability. Many authors have developed mathematical models useful in predicting sand production in reservoirs. These models have proved to be useful tools in sand control viability studies. Geomechanical parameters like principal stresses have been useful in these models. However, many of these models developed have turned out complex, requiring difficult-to-obtain parameters or having low level of accuracy when compared to observed field data. In this paper, a mathematical model was developed by modifying the work of Oluyemi and Oyeneyin (2010) who developed a simple mechanistic model requiring few and easy-to-obtain input parameters. A simple simulator, named Cassandra, was then designed using Python Programming Language so as to aid the estimation process.\u0000 After validation with field data from different reservoirs, it was found that Cassandra gave results very close to observed field data, in fact, it only possesses about 8% absolute error. The model also performed excellently when compared to existing models. This software, thus, proves to be a valuable tool in any sand production analysis.","PeriodicalId":11110,"journal":{"name":"Day 2 Tue, August 06, 2019","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72868049","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}
A. Olaniyi, Mora-Glukstad I. Miguel, Dasgupta Anindya, Amrasa Kefe
Geobody identification via Spectral Decomposition has been used to optimize the development of a reservoir for a green field (Nime – not a real name) in the shallow offshore Niger Delta. The target reservoir (NM1 – not real name) is covered by a 3D seismic data that was acquired and processed using Post Stack Time Migration technique in the late nineties. Six exploration and appraisal wells have been drilled through the reservoir to date. Stratigraphically, the reservoir is approximately a 250- feet thick high net-to-gross (0.98 – 1), high porosity (0.26 – 0.28) sandstone interpreted to be stacked channel and shoreface sediments that were deposited in marginal marine environment. Given the high net-to-gross and porosity of the reservoir and absence of any intra-reservoir fault that may compartmentalize the reservoir, the reservoir is deemed laterally continuous and connected. However, fluid contact values derived from reliable combination of gamma ray, resistivity, neutron and density logs from the wells indicate a difference of 25 feet for the oil water contact (OWC) in the reservoir. To fully understand the contrasting information viz 25ft OWC difference in a highly sandy and ‘connected’ reservoir, spectral decomposition volume attribute was generated from the 3D seismic data and analyzed to determine the reservoir architecture. The spectral decomposition workflow applied involved two basic steps: i) Spectral analyser – to determine dominant frequencies in the 3D seismic volume; and ii) Spectral decomposition – creating 3D volumes for the dominant frequencies and analyzing them with the aim of identifying geobodies (channels) and defining the reservoir architecture. Prior to carrying out the Spectral Analyser, the 3D seismic cube should be ‘cropped’ to the required area of interest (AOI) to reduce computer memory required to run the algorithm. It is also advised to run any post-processing seismic workflow (e.g. VanGogh) that will increase signal to noise ratio before spectral decomposition. This paper presents the details of the Spectral Decomposition workflow which can be applied for identification of geobodies and how its result was used to optimally plan development wells in the target reservoir to mitigate an unlikely compartmentalization of the reservoir.
{"title":"Geobody Interpretation and Its Application for Field Development","authors":"A. Olaniyi, Mora-Glukstad I. Miguel, Dasgupta Anindya, Amrasa Kefe","doi":"10.2118/198818-MS","DOIUrl":"https://doi.org/10.2118/198818-MS","url":null,"abstract":"\u0000 Geobody identification via Spectral Decomposition has been used to optimize the development of a reservoir for a green field (Nime – not a real name) in the shallow offshore Niger Delta.\u0000 The target reservoir (NM1 – not real name) is covered by a 3D seismic data that was acquired and processed using Post Stack Time Migration technique in the late nineties. Six exploration and appraisal wells have been drilled through the reservoir to date. Stratigraphically, the reservoir is approximately a 250- feet thick high net-to-gross (0.98 – 1), high porosity (0.26 – 0.28) sandstone interpreted to be stacked channel and shoreface sediments that were deposited in marginal marine environment.\u0000 Given the high net-to-gross and porosity of the reservoir and absence of any intra-reservoir fault that may compartmentalize the reservoir, the reservoir is deemed laterally continuous and connected. However, fluid contact values derived from reliable combination of gamma ray, resistivity, neutron and density logs from the wells indicate a difference of 25 feet for the oil water contact (OWC) in the reservoir. To fully understand the contrasting information viz 25ft OWC difference in a highly sandy and ‘connected’ reservoir, spectral decomposition volume attribute was generated from the 3D seismic data and analyzed to determine the reservoir architecture.\u0000 The spectral decomposition workflow applied involved two basic steps: i) Spectral analyser – to determine dominant frequencies in the 3D seismic volume; and ii) Spectral decomposition – creating 3D volumes for the dominant frequencies and analyzing them with the aim of identifying geobodies (channels) and defining the reservoir architecture.\u0000 Prior to carrying out the Spectral Analyser, the 3D seismic cube should be ‘cropped’ to the required area of interest (AOI) to reduce computer memory required to run the algorithm. It is also advised to run any post-processing seismic workflow (e.g. VanGogh) that will increase signal to noise ratio before spectral decomposition.\u0000 This paper presents the details of the Spectral Decomposition workflow which can be applied for identification of geobodies and how its result was used to optimally plan development wells in the target reservoir to mitigate an unlikely compartmentalization of the reservoir.","PeriodicalId":11110,"journal":{"name":"Day 2 Tue, August 06, 2019","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82184470","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}
Fertilizers are used to enhance the degradation and sequestration of oil-polluted environments, but a decrease in fertilizer efficiency can lead to severe environmental consequences. The aim of this study was, therefore, to formulate a slow release fertilizer using nutrient-rich, ecofriendly and readily available agricultural and industrial wastes. The formulated fertilizer was coated with a renewable, nontoxic and biodegradable material which was then tested against commercial NPK fertilizer for its effect on hydrocarbon degradation rate. Crude oil polluted soil from an artisanal refining site was used to evaluate the efficiency of the fertilizers. Next-generation sequencing technique was used to determine the microbiome of the oil-polluted soil. Metabolic fingerprints were also determined as remediation progressed. Other parameters monitored were pH, extractable total petroleum hydrocarbons (ETPH), NO3- -N, total phosphate and total potassium. Initial ETPH of the polluted soil was 16,388 mg/kg which reduced to 2,250.21 mg/kg after 56 days of remediation. The formulated fertilizer gradually led to an increase in soil pH from being slightly acidic (5.6) to near neutral (6.9), while the commercial NPK fertilizer led to a further decrease in soil pH. Both fertilizers enhanced degradation without significant differences, however, the formulated fertilizer greatly improved microbial diversity. Proteobacteria, Chloroflexi and Acidobacteria dominated the soil microbiome with Acidocella being the leading bacterial genus. Signature metabolites identified included benzenamine, cyclobutanone, octadecane and hexadecane which were all related to hydrocarbon biodegradation. The study revealed that the formulated fertilizer effectively enhanced the restoration of oil-polluted soils as well as microbial diversity and soil fertility. It also shows that acid-loving bacteria are important in the bioremediation of acidic oil-polluted soils.
{"title":"Formulation and Evaluation of Slow-Release Fertilizer from Agricultural and Industrial Wastes for Remediation of Crude Oil-Polluted Soils","authors":"C. Obieze, C. Chikere, R. Adeleke, O. Akaranta","doi":"10.2118/198815-MS","DOIUrl":"https://doi.org/10.2118/198815-MS","url":null,"abstract":"\u0000 Fertilizers are used to enhance the degradation and sequestration of oil-polluted environments, but a decrease in fertilizer efficiency can lead to severe environmental consequences. The aim of this study was, therefore, to formulate a slow release fertilizer using nutrient-rich, ecofriendly and readily available agricultural and industrial wastes. The formulated fertilizer was coated with a renewable, nontoxic and biodegradable material which was then tested against commercial NPK fertilizer for its effect on hydrocarbon degradation rate. Crude oil polluted soil from an artisanal refining site was used to evaluate the efficiency of the fertilizers. Next-generation sequencing technique was used to determine the microbiome of the oil-polluted soil. Metabolic fingerprints were also determined as remediation progressed. Other parameters monitored were pH, extractable total petroleum hydrocarbons (ETPH), NO3- -N, total phosphate and total potassium. Initial ETPH of the polluted soil was 16,388 mg/kg which reduced to 2,250.21 mg/kg after 56 days of remediation. The formulated fertilizer gradually led to an increase in soil pH from being slightly acidic (5.6) to near neutral (6.9), while the commercial NPK fertilizer led to a further decrease in soil pH. Both fertilizers enhanced degradation without significant differences, however, the formulated fertilizer greatly improved microbial diversity. Proteobacteria, Chloroflexi and Acidobacteria dominated the soil microbiome with Acidocella being the leading bacterial genus. Signature metabolites identified included benzenamine, cyclobutanone, octadecane and hexadecane which were all related to hydrocarbon biodegradation. The study revealed that the formulated fertilizer effectively enhanced the restoration of oil-polluted soils as well as microbial diversity and soil fertility. It also shows that acid-loving bacteria are important in the bioremediation of acidic oil-polluted soils.","PeriodicalId":11110,"journal":{"name":"Day 2 Tue, August 06, 2019","volume":"55 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87814111","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}
Osehojie Ojeh-Oziegbe, Y. Aye, Euan Murdoch, John Walker
Every field development faces the challenge of keeping capital and operational expenditures (CAPEX and OPEX) within reasonable limits, while at the same time exploring matured or new technology that can achieve cost limit objectives. With the uncertainties, cyclic nature, instability in the oil and gas global investment markets, and the fluctuation in crude oil pricing, operators in energy exploration and production (E&P) industries as well as service providers are constantly looking for better and more efficient cost-saving products and services. The challenge of maximizing hydrocarbon recovery in deepwater completions with minimum investment, while maintaining the highest level of health, safety, and environment (HSE) and service quality is a continual catalyst for new products and service delivery techniques. In the operator’s Bonga subsea field, the conventional completions techniques for all open hole standalone screen (SAS) completion installations are performed in multiple trips. The first trip involves running the lower completion including a gravel pack packer with screen assembly which allows a gravel pack packer service tool and an internal string with a pump-thru wash-down capability to enable toe-heel circulation, packer setting, and testing. The internal string, which is comprised of the packer setting tool, internal wash pipe, and accessories, is recovered after completion of the first trip into the open hole reservoir section. The second trip involves running the production tubing, production packer, downhole gauge mandrel, safety valve, and other completions accessories and landing the production string into the lower completion and on the tubing hanger. The major objectives and drivers for the innovative open hole single trip stand-alone screen completion (STC-SAS) in a deep offshore environment is basically to save rig costs, use proven and emerging technologies, employ completions best practices, reduce exposure of personnel to safety hazards, and reduce non-productive times (NPT). New completions techniques with different services and product providers could pose a challenge in terms of vendor interface management, equipment compatibility, and procedural integration of multiple downhole tools with different operating boundaries and limits. The STC-SAS completions concept in deep water was generated with the operator’s Wells Front End Completion and Well Intervention team in December 2015. This was driven by an opportunity to further reduce well delivery rig time which is at a premium in deepwater subsea completions. The average completions time in the field stood at 10 days per 10,000 ft well. The group was challenged to further improve the well delivery time. However, there was no benchmark as the industry data showed that a single trip open hole stand-alone screen completion had not been installed globally in a deep water subsea environment. This paper presents the evolution of the completions design, the critical chal
{"title":"Successful Installation of the First Deep Water Single Trip Stand-Alone-Screens Completion in the Industry Saves rig Time","authors":"Osehojie Ojeh-Oziegbe, Y. Aye, Euan Murdoch, John Walker","doi":"10.2118/198833-MS","DOIUrl":"https://doi.org/10.2118/198833-MS","url":null,"abstract":"Every field development faces the challenge of keeping capital and operational expenditures (CAPEX and OPEX) within reasonable limits, while at the same time exploring matured or new technology that can achieve cost limit objectives.\u0000 With the uncertainties, cyclic nature, instability in the oil and gas global investment markets, and the fluctuation in crude oil pricing, operators in energy exploration and production (E&P) industries as well as service providers are constantly looking for better and more efficient cost-saving products and services. The challenge of maximizing hydrocarbon recovery in deepwater completions with minimum investment, while maintaining the highest level of health, safety, and environment (HSE) and service quality is a continual catalyst for new products and service delivery techniques.\u0000 In the operator’s Bonga subsea field, the conventional completions techniques for all open hole standalone screen (SAS) completion installations are performed in multiple trips. The first trip involves running the lower completion including a gravel pack packer with screen assembly which allows a gravel pack packer service tool and an internal string with a pump-thru wash-down capability to enable toe-heel circulation, packer setting, and testing. The internal string, which is comprised of the packer setting tool, internal wash pipe, and accessories, is recovered after completion of the first trip into the open hole reservoir section. The second trip involves running the production tubing, production packer, downhole gauge mandrel, safety valve, and other completions accessories and landing the production string into the lower completion and on the tubing hanger.\u0000 The major objectives and drivers for the innovative open hole single trip stand-alone screen completion (STC-SAS) in a deep offshore environment is basically to save rig costs, use proven and emerging technologies, employ completions best practices, reduce exposure of personnel to safety hazards, and reduce non-productive times (NPT). New completions techniques with different services and product providers could pose a challenge in terms of vendor interface management, equipment compatibility, and procedural integration of multiple downhole tools with different operating boundaries and limits.\u0000 The STC-SAS completions concept in deep water was generated with the operator’s Wells Front End Completion and Well Intervention team in December 2015. This was driven by an opportunity to further reduce well delivery rig time which is at a premium in deepwater subsea completions. The average completions time in the field stood at 10 days per 10,000 ft well. The group was challenged to further improve the well delivery time. However, there was no benchmark as the industry data showed that a single trip open hole stand-alone screen completion had not been installed globally in a deep water subsea environment.\u0000 This paper presents the evolution of the completions design, the critical chal","PeriodicalId":11110,"journal":{"name":"Day 2 Tue, August 06, 2019","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89302239","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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