Natural Gas production and transportation are at risk of Gas hydrate plugging especially when in offshore environments where temperature is low and pressure is high. These plugs can eventually block the pipeline, increase back pressure, stop production and ultimately rupture gas pipelines. This study seeks to develops machine learning models after a kinetic inhibitor to predict the gas hydrate formation and pressure changes within the natural gas flow line. Green hydrate inhibitor A, B and C were obtained as plant extracts and applied in low dosages (0.01 wt.% to 0.1 wt.%) on a 12meter skid-mounted hydrate closed flow loop. From the data generated, the optimal dosages of inhibitor A, B and C were observed to be 0.02 wt.%, 0.06 wt.% and 0.1 wt.% respectively. The data associated with these optimal dosages were fed to a set of supervised machine learning algorithms (Extreme gradient boost, Gradient boost regressor and Linear regressor) and a deep learning algorithm (Artificial Neural Network). The output results from the set of supervised learning algorithms and Deep Learning algorithms were compared in terms of their accuracies in predicting the hydrate formation and the pressure within the natural gas flow line. All models had accuracies greater than 90%. This result show that the application Machine learning to solving flow assurance problems is viable. The results show that it is viable to apply machine learning algorithms to solve flow assurance problems, analyzing data and getting reports which can improve accuracy and speed of on-site decision making process.
{"title":"Application of Machine Learning in Gas-Hydrate Formation and Trendline Prediction","authors":"Celestine Udim Monday, T. Odutola","doi":"10.2118/208653-ms","DOIUrl":"https://doi.org/10.2118/208653-ms","url":null,"abstract":"\u0000 Natural Gas production and transportation are at risk of Gas hydrate plugging especially when in offshore environments where temperature is low and pressure is high. These plugs can eventually block the pipeline, increase back pressure, stop production and ultimately rupture gas pipelines. This study seeks to develops machine learning models after a kinetic inhibitor to predict the gas hydrate formation and pressure changes within the natural gas flow line. Green hydrate inhibitor A, B and C were obtained as plant extracts and applied in low dosages (0.01 wt.% to 0.1 wt.%) on a 12meter skid-mounted hydrate closed flow loop. From the data generated, the optimal dosages of inhibitor A, B and C were observed to be 0.02 wt.%, 0.06 wt.% and 0.1 wt.% respectively. The data associated with these optimal dosages were fed to a set of supervised machine learning algorithms (Extreme gradient boost, Gradient boost regressor and Linear regressor) and a deep learning algorithm (Artificial Neural Network). The output results from the set of supervised learning algorithms and Deep Learning algorithms were compared in terms of their accuracies in predicting the hydrate formation and the pressure within the natural gas flow line. All models had accuracies greater than 90%. This result show that the application Machine learning to solving flow assurance problems is viable. The results show that it is viable to apply machine learning algorithms to solve flow assurance problems, analyzing data and getting reports which can improve accuracy and speed of on-site decision making process.","PeriodicalId":10904,"journal":{"name":"Day 2 Tue, October 19, 2021","volume":"56 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72886503","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}
S. Hoang, T. Tran, T. N. Nguyen, T. Truong, D. Pham, T. Tran, Vinh X. Trinh, A. Ngo
This study aims to apply machine learning (ML) to make history matching (HM) process easier, faster, more accurate, and more reliable by determining whether Local Grid Refinement (LGR) with transmissibility multiplier is needed to history match gas-condensate wells producing from geologically complex reservoirs and determining how LGR should be set up to successfully history match those production wells. The main challenges for HM gas-condensate production from Hai Thach wells are large effect of condensate banking (condensate blockage), flow baffles by the sub-seismic fault network, complex reservoir distribution and connectivity, highly uncertain HIIP, and lack of PVT information for most reservoirs. In this study, ML was applied to analyze production data using synthetic samples generated by a very large number of compositional sector models so that the need for LGR could be identified before the HM process and the required LGR setup could also be determined. The proposed method helped provide better models in a much shorter time, and improved the efficiency and reliability of the dynamic modeling process. 500+ synthetic samples were generated using compositional sector models and divided into training and test sets. Supervised classification algorithms including logistic regression, Gaussian, Bernoulli, and multinomial Naïve Bayes, linear discriminant analysis, support vector machine, K-nearest neighbors, and Decision Tree as well as ANN were applied to the data sets to determine the need for using LGR in HM. The best algorithm was found to be the Decision Tree classifier, with 100% and 99% accuracy on the training and the test sets, respectively. The size of the LGR area could also be determined reasonably well at 89% and 87% accuracy on the training and the test sets, respectively. The range of the transmissibility multiplier could also be determined reasonably well at 97% and 91% accuracy on the training and the test sets, respectively. Moreover, the ML model was validated using actual production and HM data. A new method of applying ML in dynamic modeling and HM of challenging gas-condensate wells in geologically complex reservoirs has been successfully applied to the high-pressure high-temperature Hai Thach field offshore Vietnam. The proposed method helped reduce many trial and error simulation runs and provide better and more reliable dynamic models.
{"title":"Successful Application of Machine Learning to Improve Dynamic Modeling and History Matching for Complex Gas-Condensate Reservoirs in Hai Thach Field, Nam Con Son Basin, Offshore Vietnam","authors":"S. Hoang, T. Tran, T. N. Nguyen, T. Truong, D. Pham, T. Tran, Vinh X. Trinh, A. Ngo","doi":"10.2118/208657-ms","DOIUrl":"https://doi.org/10.2118/208657-ms","url":null,"abstract":"\u0000 This study aims to apply machine learning (ML) to make history matching (HM) process easier, faster, more accurate, and more reliable by determining whether Local Grid Refinement (LGR) with transmissibility multiplier is needed to history match gas-condensate wells producing from geologically complex reservoirs and determining how LGR should be set up to successfully history match those production wells.\u0000 The main challenges for HM gas-condensate production from Hai Thach wells are large effect of condensate banking (condensate blockage), flow baffles by the sub-seismic fault network, complex reservoir distribution and connectivity, highly uncertain HIIP, and lack of PVT information for most reservoirs. In this study, ML was applied to analyze production data using synthetic samples generated by a very large number of compositional sector models so that the need for LGR could be identified before the HM process and the required LGR setup could also be determined. The proposed method helped provide better models in a much shorter time, and improved the efficiency and reliability of the dynamic modeling process.\u0000 500+ synthetic samples were generated using compositional sector models and divided into training and test sets. Supervised classification algorithms including logistic regression, Gaussian, Bernoulli, and multinomial Naïve Bayes, linear discriminant analysis, support vector machine, K-nearest neighbors, and Decision Tree as well as ANN were applied to the data sets to determine the need for using LGR in HM. The best algorithm was found to be the Decision Tree classifier, with 100% and 99% accuracy on the training and the test sets, respectively. The size of the LGR area could also be determined reasonably well at 89% and 87% accuracy on the training and the test sets, respectively. The range of the transmissibility multiplier could also be determined reasonably well at 97% and 91% accuracy on the training and the test sets, respectively. Moreover, the ML model was validated using actual production and HM data.\u0000 A new method of applying ML in dynamic modeling and HM of challenging gas-condensate wells in geologically complex reservoirs has been successfully applied to the high-pressure high-temperature Hai Thach field offshore Vietnam. The proposed method helped reduce many trial and error simulation runs and provide better and more reliable dynamic models.","PeriodicalId":10904,"journal":{"name":"Day 2 Tue, October 19, 2021","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88040803","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}
Distributed acoustic sensing (DAS) has been used in the oil and gas industry as an advanced technology for surveillance and diagnostics. Operators use DAS to monitor hydraulic fracturing activities, to examine well stimulation efficacy, and to estimate complex fracture system geometries. Particularly, low-frequency DAS can detect geomechanical events such as fracture-hits as hydraulic fractures propagate and create strain rate variations. Analysis of DAS data today is mostly done post-job and subject to interpretation methods. However, the continuous and dense data stream generated live by DAS offers the opportunity for more efficient and accurate real-time data-driven analysis. The objective of this study is to develop a machine learning-based workflow that can identify and locate fracture-hit events in simulated strain rate response that is correlated with low-frequency DAS data. In this paper, "fracture-hit" refers to a hydraulic fracture originated from a stimulated well intersecting an offset well. We start with building a single fracture propagation model to produce strain rate patterns observed at a hypothetical monitoring well. This model is then used to generate two sets of strain rate responses with one set containing fracture-hit events. The labeled synthetic data are then used to train a custom convolutional neural network (CNN) model for identifying the presence of fracture-hit events. The same model is trained again for locating the event with the output layer of the model replaced with linear units. We achieved near-perfect predictions for both event classification and localization. These promising results prove the feasibility of using CNN for real-time event detection from fiber optic sensing data. Additionally, we used image analysis techniques, including edge detection, for recognizing fracture-hit event patterns in strain rate images. The accuracy is also plausible, but edge detection is more dependent on image quality, hence less robust compared to CNN models. This comparison further supports the need for CNN applications in image-based real-time fiber optic sensing event detection.
{"title":"Classification and Localization of Low-Frequency DAS Strain Rate Patterns with Convolutional Neural Networks","authors":"Mengyuan Chen, Jin Tang, D. Zhu, A. Daniel Hill","doi":"10.2118/205136-ms","DOIUrl":"https://doi.org/10.2118/205136-ms","url":null,"abstract":"\u0000 Distributed acoustic sensing (DAS) has been used in the oil and gas industry as an advanced technology for surveillance and diagnostics. Operators use DAS to monitor hydraulic fracturing activities, to examine well stimulation efficacy, and to estimate complex fracture system geometries. Particularly, low-frequency DAS can detect geomechanical events such as fracture-hits as hydraulic fractures propagate and create strain rate variations. Analysis of DAS data today is mostly done post-job and subject to interpretation methods. However, the continuous and dense data stream generated live by DAS offers the opportunity for more efficient and accurate real-time data-driven analysis. The objective of this study is to develop a machine learning-based workflow that can identify and locate fracture-hit events in simulated strain rate response that is correlated with low-frequency DAS data. In this paper, \"fracture-hit\" refers to a hydraulic fracture originated from a stimulated well intersecting an offset well. We start with building a single fracture propagation model to produce strain rate patterns observed at a hypothetical monitoring well. This model is then used to generate two sets of strain rate responses with one set containing fracture-hit events. The labeled synthetic data are then used to train a custom convolutional neural network (CNN) model for identifying the presence of fracture-hit events. The same model is trained again for locating the event with the output layer of the model replaced with linear units. We achieved near-perfect predictions for both event classification and localization. These promising results prove the feasibility of using CNN for real-time event detection from fiber optic sensing data. Additionally, we used image analysis techniques, including edge detection, for recognizing fracture-hit event patterns in strain rate images. The accuracy is also plausible, but edge detection is more dependent on image quality, hence less robust compared to CNN models. This comparison further supports the need for CNN applications in image-based real-time fiber optic sensing event detection.","PeriodicalId":10904,"journal":{"name":"Day 2 Tue, October 19, 2021","volume":"36 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86136678","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 common industry practice is to select a particular model from a set of models to history match oil production and estimate reserves by extrapolation. Future production forecasting is usually done in this deterministic way. However, this approach neglects: a) model uncertainty, and b) quantification of uncertainty of future production forecasts. The current study evaluates the predictive accuracy of rate-time models to forecast production over a set of tight oil wells of West Texas. We present the application of an accuracy metric that evaluates the uncertainty of our models' estimates: the expected log predictive density (elpd). This work assesses the predictive performance of two empirical models—the Arps hyperbolic and the logistic growth models—and two physics-based models—scaled slightly compressible single-phase and scaled two-phase (oil and gas) solutions of the diffusivity equation. These models are arbitrarily selected for the purpose of illustrating the statistical procedure shown in this paper. First, we perform classical regression with the models and evaluate their predictive performance using frequentist (point estimates) metrics such as R2, the Akaike information criteria (AIC), and hindcasting. Second, we generate probabilistic production forecasts using Bayesian inference for each model. Third, we evaluate the predictive accuracy of the models using the elpd accuracy metric. This metric evaluates a measure of out-of-sample predictive performance. We apply both adjusted-within-sample and cross-validation techniques. The adjusted within-sample method is the widely applicable information criteria (WAIC). The cross-validation techniques are hindcasting and leave-one-out (LOO-CV) method. The results of this research are the following. First, we illustrate that the assessment of a model's predictive accuracy depends on whether we use frequentist or Bayesian approaches. This is an important finding in this work. The frequentist approach relies on point estimates while the Bayesian approach considers the uncertainty of our models' estimates. From a frequentist or classical standpoint, all of the models under study yielded very similar results which made it difficult to determine which model yielded the best predictive performance. From a Bayesian standpoint, however, we determined that the logistic growth model yielded a best match in 81 of 130 wells in our sample play and the two-phase physics-based model yielded a best match in 39 of the wells. In addition, we show that WAIC and LOO-CV present similar results for each model, a thing to expect because of their asymptotical equivalence. Finally, Our observations regarding the different models are subject to the dataset under study wherein a majority of the wells are in transient flow. The present study provides tools to evaluate the predictive accuracy of models used to forecast (extrapolate) production of tight oil wells. The elpd is an accuracy metric useful to evaluate the uncer
{"title":"Bayesian Predictive Performance Assessment of Rate-Time Models for Unconventional Production Forecasting","authors":"L. R. Maraggi, L. Lake, M. P. Walsh","doi":"10.2118/205151-ms","DOIUrl":"https://doi.org/10.2118/205151-ms","url":null,"abstract":"\u0000 A common industry practice is to select a particular model from a set of models to history match oil production and estimate reserves by extrapolation. Future production forecasting is usually done in this deterministic way. However, this approach neglects: a) model uncertainty, and b) quantification of uncertainty of future production forecasts. The current study evaluates the predictive accuracy of rate-time models to forecast production over a set of tight oil wells of West Texas. We present the application of an accuracy metric that evaluates the uncertainty of our models' estimates: the expected log predictive density (elpd).\u0000 This work assesses the predictive performance of two empirical models—the Arps hyperbolic and the logistic growth models—and two physics-based models—scaled slightly compressible single-phase and scaled two-phase (oil and gas) solutions of the diffusivity equation. These models are arbitrarily selected for the purpose of illustrating the statistical procedure shown in this paper. First, we perform classical regression with the models and evaluate their predictive performance using frequentist (point estimates) metrics such as R2, the Akaike information criteria (AIC), and hindcasting. Second, we generate probabilistic production forecasts using Bayesian inference for each model. Third, we evaluate the predictive accuracy of the models using the elpd accuracy metric. This metric evaluates a measure of out-of-sample predictive performance. We apply both adjusted-within-sample and cross-validation techniques. The adjusted within-sample method is the widely applicable information criteria (WAIC). The cross-validation techniques are hindcasting and leave-one-out (LOO-CV) method.\u0000 The results of this research are the following. First, we illustrate that the assessment of a model's predictive accuracy depends on whether we use frequentist or Bayesian approaches. This is an important finding in this work. The frequentist approach relies on point estimates while the Bayesian approach considers the uncertainty of our models' estimates. From a frequentist or classical standpoint, all of the models under study yielded very similar results which made it difficult to determine which model yielded the best predictive performance. From a Bayesian standpoint, however, we determined that the logistic growth model yielded a best match in 81 of 130 wells in our sample play and the two-phase physics-based model yielded a best match in 39 of the wells. In addition, we show that WAIC and LOO-CV present similar results for each model, a thing to expect because of their asymptotical equivalence. Finally, Our observations regarding the different models are subject to the dataset under study wherein a majority of the wells are in transient flow.\u0000 The present study provides tools to evaluate the predictive accuracy of models used to forecast (extrapolate) production of tight oil wells. The elpd is an accuracy metric useful to evaluate the uncer","PeriodicalId":10904,"journal":{"name":"Day 2 Tue, October 19, 2021","volume":"55 27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82418885","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}
Hilario Martin Rodriguez, Y. Barzin, G. Walker, M. Gruenwalder, Matias Fernandez-Badessich, M. Manohar
This study has double objectives: investigation of the main recovery mechanisms affecting the performance of the gas huff-n-puff (GHnP) process in a shale oil reservoir, and application of optimization techniques to modelling of the cyclic gas injection. A dual-permeability reservoir simulation model has been built to reproduce the performance of a single hydraulic fracture. The hydraulic fracture has the average geometry and properties of the well under analysis. A history match workflow has been run to obtain a simulation model fully representative of the studied well. An optimization workflow has been run to maximize the cumulative oil obtained during the GHnP process. The operational variables optimized are: duration of gas injection, soaking, and production, onset time of GHnP, injection gas flow rate, and number of cycles. This optimization workflow is launched twice using two different compositions for the injection gas: rich gas and pure methane. Additionally, the optimum case obtained previously with rich gas is simulated with a higher minimum bottom hole pressure (BHP) for both primary production and GHnP process. Moreover, some properties that could potentially explain the different recovery mechanisms were tracked and analyzed. Three different porosity systems have been considered in the model: fractures, matrix in the stimulated reservoir volume (SRV), and matrix in the non-SRV zone (virgin matrix). Each one with a different pressure profile, and thus with its corresponding recovery mechanisms, identified as below: Vaporization/Condensation (two-phase system) in the fractures.Miscibility (liquid single-phase) in the non-SRV matrix.Miscibility and/or Vaporization/Condensation in the SRV matrix: depending on the injection gas composition and the pressure profile along the SRV the mechanism may be clearly one of them or even both. Results of this simulation study suggest that for the optimized cases, incremental oil recovery is 24% when the gas injected is a rich gas, but it is only 2.4% when the gas injected is pure methane. A higher incremental oil recovery of 49% is obtained, when injecting rich gas and increasing the minimum BHP of the puff cycle above the saturation pressure. Injection of gas results in reduction of oil molecular weight, oil density and oil viscosity in the matrix, i.e., the oil gets lighter. This net decrease is more pronounced in the SRV than in the non-SRV region. The incremental oil recovery observed in the GHnP process is due to the mobilization of heavy components (not present in the injection gas composition) that otherwise would remain inside the reservoir. Due to the main characteristic of the shale reservoirs (nano-Darcy permeability), GHnP is not a displacement process. A key factor in success of the GHnP process is to improve the contact of the injected gas and the reservoir oil to increase the mixing and mass transfer. This study includes a review of different mechanisms, and specifically tracks
{"title":"An In-Depth Review of the Recovery Mechanisms for the Cyclic Gas Injection Process in Shale Oil Reservoirs","authors":"Hilario Martin Rodriguez, Y. Barzin, G. Walker, M. Gruenwalder, Matias Fernandez-Badessich, M. Manohar","doi":"10.2118/205194-ms","DOIUrl":"https://doi.org/10.2118/205194-ms","url":null,"abstract":"\u0000 This study has double objectives: investigation of the main recovery mechanisms affecting the performance of the gas huff-n-puff (GHnP) process in a shale oil reservoir, and application of optimization techniques to modelling of the cyclic gas injection.\u0000 A dual-permeability reservoir simulation model has been built to reproduce the performance of a single hydraulic fracture. The hydraulic fracture has the average geometry and properties of the well under analysis. A history match workflow has been run to obtain a simulation model fully representative of the studied well. An optimization workflow has been run to maximize the cumulative oil obtained during the GHnP process. The operational variables optimized are: duration of gas injection, soaking, and production, onset time of GHnP, injection gas flow rate, and number of cycles. This optimization workflow is launched twice using two different compositions for the injection gas: rich gas and pure methane. Additionally, the optimum case obtained previously with rich gas is simulated with a higher minimum bottom hole pressure (BHP) for both primary production and GHnP process. Moreover, some properties that could potentially explain the different recovery mechanisms were tracked and analyzed.\u0000 Three different porosity systems have been considered in the model: fractures, matrix in the stimulated reservoir volume (SRV), and matrix in the non-SRV zone (virgin matrix). Each one with a different pressure profile, and thus with its corresponding recovery mechanisms, identified as below: Vaporization/Condensation (two-phase system) in the fractures.Miscibility (liquid single-phase) in the non-SRV matrix.Miscibility and/or Vaporization/Condensation in the SRV matrix: depending on the injection gas composition and the pressure profile along the SRV the mechanism may be clearly one of them or even both.\u0000 Results of this simulation study suggest that for the optimized cases, incremental oil recovery is 24% when the gas injected is a rich gas, but it is only 2.4% when the gas injected is pure methane. A higher incremental oil recovery of 49% is obtained, when injecting rich gas and increasing the minimum BHP of the puff cycle above the saturation pressure. Injection of gas results in reduction of oil molecular weight, oil density and oil viscosity in the matrix, i.e., the oil gets lighter. This net decrease is more pronounced in the SRV than in the non-SRV region. The incremental oil recovery observed in the GHnP process is due to the mobilization of heavy components (not present in the injection gas composition) that otherwise would remain inside the reservoir.\u0000 Due to the main characteristic of the shale reservoirs (nano-Darcy permeability), GHnP is not a displacement process. A key factor in success of the GHnP process is to improve the contact of the injected gas and the reservoir oil to increase the mixing and mass transfer. This study includes a review of different mechanisms, and specifically tracks ","PeriodicalId":10904,"journal":{"name":"Day 2 Tue, October 19, 2021","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90815273","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 : 2021-10-02DOI: 10.21475/ajcs.21.15.10.p3221
V. Esan, O. O. Omilani, Yewande Omoronike Osuntoyinbo, Goodness Toluwanimi Olutayo, T. Sangoyomi
Drought stress is an environmental factor which restraints crop production and quality worldwide. It is now undeniable that drought limits the performance of crop plants. Annual water resources decline due to low rainfall and the reduction of the number of days of rainfall. The objectives were to: (1) screen existing cowpea genotypes at germination and seedling stages for their adaptation to water stress and (2) identify tolerant cowpea varieties to drought. The experiments were carried out both in the laboratory using an osmotic stress (laboratory drought stress) induced by polyethylene glycol 6000 (PEG 6000) and in an open field under different levels (control, moderate and severe) of drought conditions. Fourteen Cowpea varieties were used in this study. The drought stress was imposed on 21-days old seedlings and the experiment lasted for 3 months. In the laboratory, four treatments 0%, 6.5%, 13% and 16.5% PEG were used while in the open field two drought levels were imposed. The two experiments were laid out in randomized complete block design with three replications. Morphological, physiological and agronomic data were collected. Results showed that at high concentration (16.50% PEG6000), high germination percentage was recorded in Raphael variety (88%) followed by Tawa (71.11%) and Eginwogogo (60%) whereas germination was completely inhibited in ITG7K-449-35 variety. The morphological traits measured such as plant height, leaf width, leaf length was reduced by drought stress. The highest reduction (47%) was recorded in the leaf width of Tiligre variety. In the second year of the experiment, IT99K-573-2-1 and Eginwogogo varieties plants died after 20 days of drought treatment because it could not withstand the drought stress condition during harmattan (a dry and dusty wind in West Africa) period due to the rapid dryness of soil moisture content. The results of dendrogram revealed that Raphael and Tawa were the most tolerant varieties
{"title":"Assessment of Harmattan weather on cowpea (Vigna unguiculata, (L.) Walp.) production under drought stress","authors":"V. Esan, O. O. Omilani, Yewande Omoronike Osuntoyinbo, Goodness Toluwanimi Olutayo, T. Sangoyomi","doi":"10.21475/ajcs.21.15.10.p3221","DOIUrl":"https://doi.org/10.21475/ajcs.21.15.10.p3221","url":null,"abstract":"Drought stress is an environmental factor which restraints crop production and quality worldwide. It is now undeniable that drought limits the performance of crop plants. Annual water resources decline due to low rainfall and the reduction of the number of days of rainfall. The objectives were to: (1) screen existing cowpea genotypes at germination and seedling stages for their adaptation to water stress and (2) identify tolerant cowpea varieties to drought. The experiments were carried out both in the laboratory using an osmotic stress (laboratory drought stress) induced by polyethylene glycol 6000 (PEG 6000) and in an open field under different levels (control, moderate and severe) of drought conditions. Fourteen Cowpea varieties were used in this study. The drought stress was imposed on 21-days old seedlings and the experiment lasted for 3 months. In the laboratory, four treatments 0%, 6.5%, 13% and 16.5% PEG were used while in the open field two drought levels were imposed. The two experiments were laid out in randomized complete block design with three replications. Morphological, physiological and agronomic data were collected. Results showed that at high concentration (16.50% PEG6000), high germination percentage was recorded in Raphael variety (88%) followed by Tawa (71.11%) and Eginwogogo (60%) whereas germination was completely inhibited in ITG7K-449-35 variety. The morphological traits measured such as plant height, leaf width, leaf length was reduced by drought stress. The highest reduction (47%) was recorded in the leaf width of Tiligre variety. In the second year of the experiment, IT99K-573-2-1 and Eginwogogo varieties plants died after 20 days of drought treatment because it could not withstand the drought stress condition during harmattan (a dry and dusty wind in West Africa) period due to the rapid dryness of soil moisture content. The results of dendrogram revealed that Raphael and Tawa were the most tolerant varieties","PeriodicalId":10904,"journal":{"name":"Day 2 Tue, October 19, 2021","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82120668","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 : 2021-10-02DOI: 10.21475/ajcs.21.15.10.p3264
Ayalew Ligaba- Osena, Mitiku A. Mengistu, G. Beyene, John Cushman, R. Glahn, M. Piñeros
Tef (Eragrostis tef) is an underutilized food crop rich in minerals, vitamins, and amino acids. However, mineral profiling of diverse tef accessions, and estimation of bioavailable iron from tef has been lacking. In this study, we analyzed the mineral content of 41 tef accessions along with major cereals. Our analysis revealed that tef seeds contain significantly more minerals than maize, rice, and the wheat varieties used in this study. A significant variation in mineral content was also observed across the tef accessions. We also performed a relative estimation of Fe bioavailability from selected tef accessions and reference crops using an established Caco-2 cell bioassay. This bioassay measures human intestinal cell Fe uptake via intracellular ferritin formation, a storage protein that is a validated marker of Fe uptake. Higher levels of Fe uptake were observed in the PI-494307, PI-494425, and PI-195937 accessions, than those recorded in cells fed wheat, rice, or tef accessions PI-329681, PI-494408 and PI-494293. There was no marked difference in phytic acid (PA) content between tef and wheat, while the PA level in rice was lower than tef and wheat. Enhanced Fe uptake evident in tef accession PI494425 could not be explained by seed Fe content. The Fe content of PI-494425 was lower than the other tef accessions, suggesting that other factors control the amount of bioavailable Fe from tef. Considerable variation in mineral content and bioavailable Fe between tef and other cereals indicate a potential for improving mineral nutrition from this vital food crop
{"title":"Grain mineral nutrient profiling and iron bioavailability of an ancient crop tef (Eragrostis tef)","authors":"Ayalew Ligaba- Osena, Mitiku A. Mengistu, G. Beyene, John Cushman, R. Glahn, M. Piñeros","doi":"10.21475/ajcs.21.15.10.p3264","DOIUrl":"https://doi.org/10.21475/ajcs.21.15.10.p3264","url":null,"abstract":"Tef (Eragrostis tef) is an underutilized food crop rich in minerals, vitamins, and amino acids. However, mineral profiling of diverse tef accessions, and estimation of bioavailable iron from tef has been lacking. In this study, we analyzed the mineral content of 41 tef accessions along with major cereals. Our analysis revealed that tef seeds contain significantly more minerals than maize, rice, and the wheat varieties used in this study. A significant variation in mineral content was also observed across the tef accessions. We also performed a relative estimation of Fe bioavailability from selected tef accessions and reference crops using an established Caco-2 cell bioassay. This bioassay measures human intestinal cell Fe uptake via intracellular ferritin formation, a storage protein that is a validated marker of Fe uptake. Higher levels of Fe uptake were observed in the PI-494307, PI-494425, and PI-195937 accessions, than those recorded in cells fed wheat, rice, or tef accessions PI-329681, PI-494408 and PI-494293. There was no marked difference in phytic acid (PA) content between tef and wheat, while the PA level in rice was lower than tef and wheat. Enhanced Fe uptake evident in tef accession PI494425 could not be explained by seed Fe content. The Fe content of PI-494425 was lower than the other tef accessions, suggesting that other factors control the amount of bioavailable Fe from tef. Considerable variation in mineral content and bioavailable Fe between tef and other cereals indicate a potential for improving mineral nutrition from this vital food crop","PeriodicalId":10904,"journal":{"name":"Day 2 Tue, October 19, 2021","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88572712","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 : 2021-10-02DOI: 10.21475/ajcs.21.15.10.p3337
Endrizal Endrizal, J. Bobihoe, J. Hendri, A. Meilin, Jumakir Jumakir, Busra B Saidi
Growing sugarcane in a double row planting system is one way to increase the productivity and sugar cane yield. Intercropping within sugarcane crops can increase the growth and productivity of sugarcane. This study aims to increase the productivity of sugarcane by adding value to potato cropping. The study used Randomized Block Design, where the treatments ae as follows: sugar cane as a planting system (A), double castor planting system (PtoP 210/50 cm) with cuttings of sugarcane stem + potato’s (B); double distance planting system (PtoP 185/50 cm) with cuttings stem sugarcane + potato’s (C); double distance planting system (PtoP 160/50 cm) with cuttings sugarcane stem + potato’s (D); double distance wedge system (PtoP 135/50 cm) with cuttings of sugarcane stem + potato. The planting system (PtoP 110/50 cm) with cuttings of sugarcane stem without planting potato was considered as control (E). All planting systems were repeated four times. The results of the study showed that the agronomic growth of sugar cane crops in some planting systems is not different, but in C and D planting systems, the number of leaves and the number of tillers were higher compared to others. Potatoes crop production in planting systems C reached 11,880 tons ha-1, which is higher than the production of planting systems A (8,640 tons ha-1.) and planting systems B (8,400 tons ha-1). After combining the determining factors of sugar cane production, the C planting systems is recommended for development of sugarcane crops because is better than other planting systems. The population of sugar cane plants in the C planting systems reached 18,000 clumps of plants per hectare
{"title":"Intercropping of potato within sugarcane plants in a double row planting system under wet climate","authors":"Endrizal Endrizal, J. Bobihoe, J. Hendri, A. Meilin, Jumakir Jumakir, Busra B Saidi","doi":"10.21475/ajcs.21.15.10.p3337","DOIUrl":"https://doi.org/10.21475/ajcs.21.15.10.p3337","url":null,"abstract":"Growing sugarcane in a double row planting system is one way to increase the productivity and sugar cane yield. Intercropping within sugarcane crops can increase the growth and productivity of sugarcane. This study aims to increase the productivity of sugarcane by adding value to potato cropping. The study used Randomized Block Design, where the treatments ae as follows: sugar cane as a planting system (A), double castor planting system (PtoP 210/50 cm) with cuttings of sugarcane stem + potato’s (B); double distance planting system (PtoP 185/50 cm) with cuttings stem sugarcane + potato’s (C); double distance planting system (PtoP 160/50 cm) with cuttings sugarcane stem + potato’s (D); double distance wedge system (PtoP 135/50 cm) with cuttings of sugarcane stem + potato. The planting system (PtoP 110/50 cm) with cuttings of sugarcane stem without planting potato was considered as control (E). All planting systems were repeated four times. The results of the study showed that the agronomic growth of sugar cane crops in some planting systems is not different, but in C and D planting systems, the number of leaves and the number of tillers were higher compared to others. Potatoes crop production in planting systems C reached 11,880 tons ha-1, which is higher than the production of planting systems A (8,640 tons ha-1.) and planting systems B (8,400 tons ha-1). After combining the determining factors of sugar cane production, the C planting systems is recommended for development of sugarcane crops because is better than other planting systems. The population of sugar cane plants in the C planting systems reached 18,000 clumps of plants per hectare","PeriodicalId":10904,"journal":{"name":"Day 2 Tue, October 19, 2021","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84597539","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 : 2021-10-02DOI: 10.21475/ajcs.21.15.10.p3182
Amanda Gabriela Paiva Carréra, Rodrigo Oliveira Aguiar, R. Cunha, I. V. Oliveira, Priscilla Diniz Lima da Silva Bernardino, C. R. D. Silva, F. I. Carvalho, C. F. Neto, M. A. S. D. Santos, J. T. D. Oliveira, P. A. Silva, E. Cunha
Cassava has importance as a source of human and animal food. With the objectives to select promising sweet and bitter cassava varieties for breeding programs, 27 genotypes were characterized in terms of their quantitative and qualitative properties. Roots were harvested from three plants per genotype, washed, peeled, sanitized. Regarding the yield, the storage root number (SRN), and the fresh storage root weight (FSRW), were determined, as well as the root fresh matter content (RFMC), and root dry matter content (RDMC), both expressed as a percentage. Among the cassava genotypes, the protein content ranged from 0.1-0.7%; lipids 0.3-2.1%; moisture 58.0-65.2%; 0.1-1.0% ash; fibers 0.9-1.9%; acidity 1,1-2,7%; pH 6.3-6.8; TSS between 0.8-1.2 ºBrix; glucose 0.1-0.8% and sucrose 0.5-1.0%, except for the fructose and starch contents, which did not vary significantly. The principal component analysis showed that the factors explain 84.2% of the total variability and through cluster analysis, evidencing cluster III for the highest starch yield and cluster I for the highest average of lipids and proteins
{"title":"Characterization of sweet and bitter cassava (Manihot esculenta Crantz) genotypes through multivariate analysis","authors":"Amanda Gabriela Paiva Carréra, Rodrigo Oliveira Aguiar, R. Cunha, I. V. Oliveira, Priscilla Diniz Lima da Silva Bernardino, C. R. D. Silva, F. I. Carvalho, C. F. Neto, M. A. S. D. Santos, J. T. D. Oliveira, P. A. Silva, E. Cunha","doi":"10.21475/ajcs.21.15.10.p3182","DOIUrl":"https://doi.org/10.21475/ajcs.21.15.10.p3182","url":null,"abstract":"Cassava has importance as a source of human and animal food. With the objectives to select promising sweet and bitter cassava varieties for breeding programs, 27 genotypes were characterized in terms of their quantitative and qualitative properties. Roots were harvested from three plants per genotype, washed, peeled, sanitized. Regarding the yield, the storage root number (SRN), and the fresh storage root weight (FSRW), were determined, as well as the root fresh matter content (RFMC), and root dry matter content (RDMC), both expressed as a percentage. Among the cassava genotypes, the protein content ranged from 0.1-0.7%; lipids 0.3-2.1%; moisture 58.0-65.2%; 0.1-1.0% ash; fibers 0.9-1.9%; acidity 1,1-2,7%; pH 6.3-6.8; TSS between 0.8-1.2 ºBrix; glucose 0.1-0.8% and sucrose 0.5-1.0%, except for the fructose and starch contents, which did not vary significantly. The principal component analysis showed that the factors explain 84.2% of the total variability and through cluster analysis, evidencing cluster III for the highest starch yield and cluster I for the highest average of lipids and proteins","PeriodicalId":10904,"journal":{"name":"Day 2 Tue, October 19, 2021","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79748188","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 : 2021-10-02DOI: 10.21475/ajcs.21.15.10.p3101
B. L. Santos, Jaína Geovana Figueiredo Lima Santos, A. E. M. D. M. Teodosio, Josivalter Araújo de Farias, M. Bonfim, C. C. Costa, K. P. Lopes
Tomatoes have a prominent market position, providing various healthy compounds. Besides the ample fresh consumption, several tomato derivatives have great interest in worldwide culinary. However, this vegetable has a short post-harvest life due to its climacteric metabolism, impairing its consumption viability. In this context, studies to mitigate post-harvest losses are frequent, where edible coatings are alternatives to prolong the shelflife of food. Here we show the efficiency of using edible coating based on arrowroot starch and chitosan in conservation the post-harvest quality of tomatoes. Our results indicate that the arrowroot starch edible coating at 3% is able to prolong the shelflife and promote the safe consumption of this vegetable
{"title":"Chitosan and arrowroot-based coatings increase shelf life and post-harvest quality of tomatoes","authors":"B. L. Santos, Jaína Geovana Figueiredo Lima Santos, A. E. M. D. M. Teodosio, Josivalter Araújo de Farias, M. Bonfim, C. C. Costa, K. P. Lopes","doi":"10.21475/ajcs.21.15.10.p3101","DOIUrl":"https://doi.org/10.21475/ajcs.21.15.10.p3101","url":null,"abstract":"Tomatoes have a prominent market position, providing various healthy compounds. Besides the ample fresh consumption, several tomato derivatives have great interest in worldwide culinary. However, this vegetable has a short post-harvest life due to its climacteric metabolism, impairing its consumption viability. In this context, studies to mitigate post-harvest losses are frequent, where edible coatings are alternatives to prolong the shelflife of food. Here we show the efficiency of using edible coating based on arrowroot starch and chitosan in conservation the post-harvest quality of tomatoes. Our results indicate that the arrowroot starch edible coating at 3% is able to prolong the shelflife and promote the safe consumption of this vegetable","PeriodicalId":10904,"journal":{"name":"Day 2 Tue, October 19, 2021","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86271872","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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