Research Article
Empirical Model for Predicting Gas Hydrate Formation in Gas Pipelines
Ikeh Lesor*,
Choko, Kingdom Onyemuche,
Oghale Victory
Issue:
Volume 7, Issue 2, December 2023
Pages:
22-34
Received:
12 May 2023
Accepted:
9 June 2023
Published:
29 November 2023
Abstract: Natural gas production and processing covers from gas reservoir to processing facility. The former is the upstream of natural gas and it involves subsurface activities of the natural gas production. The latter is the downstream of natural gas and it involves surface processing of the natural gas. Natural gas hydrate formation occurs at the subsurface, but much concern is on the downstream of natural gas processing. In fact, the processing of the natural gas is to reduce the concentration of unwanted component in the gas stream, to avoid flow assurance issues when transporting the gas through pipelines. Hydrate formations affect gas flow rate and increase operating cost. Predicting hydrate formation condition, will enable gas pipeline operators to operate the facility to avoid hydrate formation. In this study, an empirical model was developed to predict hydrate formation temperature in gas pipeline. The independent variable for the model were pressure, gas specific gravity and methane composition (which existing models does not consider) and the target variable is temperature. Different functions (logarithmic, polynomial, exponential etc) were tested for the model and the best fit for the model were logarithmic and polynomial functions. This agreed with existing models which has either only logarithmic or polynomial functions. The results obtained from the developed nonlinear empirical model shows that the R-squared was 0.94 and the errors (residuals) between the observed and predicted temperature were scattered around zero. The model compares well with existing models, especially with model that contains logarithmic and polynomial function. The nonlinear empirical model has the capability to predict very low temperature of hydrate formation. It can be used as a first check in predicting gas hydrate formation temperature in pipeline, given the pressure, gas specific gravity and composition of the gas.
Abstract: Natural gas production and processing covers from gas reservoir to processing facility. The former is the upstream of natural gas and it involves subsurface activities of the natural gas production. The latter is the downstream of natural gas and it involves surface processing of the natural gas. Natural gas hydrate formation occurs at the subsurfa...
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Research Article
Research on the Application of Bayesian Machine Learning in Reservoir Prediction
Zhiguo Fu*,
Kang Chen,
Juan Liao,
Long Long,
Da Peng
Issue:
Volume 7, Issue 2, December 2023
Pages:
35-42
Received:
28 October 2023
Accepted:
21 November 2023
Published:
29 November 2023
Abstract: As exploration and development of various types of oil and natural gas reservoirs underground continue to evolve, the more complex geological environment for seismic exploration in oil and gas reservoirs become focus increasingly. When the target reservoir is vertically interbedded and nested with various lithological strata, and exhibits poor lateral continuity, it becomes increasingly difficult for humans to distinguish oil and gas reservoirs from such complex backgrounds. It's also challenging to quantitatively assess and determine the accuracy of discrimination and achieve optimal reservoir identification results. In response to this issue, the Bayesian machine learning algorithm is introduced for automated target discrimination, enabling efficient differentiation between dolomite reservoirs, mudstones, and other lithological intercalations. The core of applying the Bayesian classifier is to establish a distribution model for target parameters, which is usually assumed to be a known distribution type such as Gaussian or Cauchy distribution. However, in petroleum seismic exploration, the distribution of oil and gas reservoir parameters is highly irregular and significantly different from these established distribution types, limiting the application of the Bayesian classification method. Therefore, we propose using a radial basis function neural network to estimate the prior distribution probability density of oil and gas reservoir parameters. This approach does not assume the prior distribution to be a certain predetermined model but instead builds the prior distribution model based on the numerical distribution characteristics of the target parameters themselves, enhancing the practicality of the Bayesian classification method. This method replaces manual reservoir identification processes, achieving high-precision, quantitative, and automated discrimination of reservoirs. Applied to actual seismic exploration data in oil fields for gas layer prediction, the discrimination results match with industrial gas wells, demonstrating the feasibility and effectiveness of the method.
Abstract: As exploration and development of various types of oil and natural gas reservoirs underground continue to evolve, the more complex geological environment for seismic exploration in oil and gas reservoirs become focus increasingly. When the target reservoir is vertically interbedded and nested with various lithological strata, and exhibits poor late...
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Research Article
Effects of Optimized Physical Vibration Parameters on Enhanced Oil Recovery
Tarek Ahmed Abdel Aal*,
Mostafa Oraby,
Marwan Moussa
Issue:
Volume 7, Issue 2, December 2023
Pages:
43-49
Received:
28 November 2023
Accepted:
19 December 2023
Published:
28 December 2023
Abstract: In this paper the optimized vibro-seismic parameters for enhancing the productivity of the oil wells through water flooding is studied and presented. The main idea of the vibro-seismic application in the petroleum industry is to provide energy to the reservoir rock through the use of either physical vibration or acoustic waves vibrations to enhance the rock and rock-fluid properties which in turn may result in improving the oil recovery. The optimized parameters that are investigated here included frequency, amplitude (energy) and the vibration duration (time). It was found that the optimization of these three parameters is essential to make sure that the vibro-seismic treatment will enhance the rock properties or otherwise it may result in damaging the reservoir. Multiple core plugs with different porosities and permeabilities are used to investigate the effect of the vibro-seismic on the enhanced oil recovery for different reservoirs. Also, multiple combinations of parameters; frequency, amplitude and time are used and the optimum combinations for the different reservoir properties are obtained. The steps that are taken in this paper for the optimization is to compare the effect in the water flooding results with different vibration parameters to that obtained without vibrations to select the optimum vibration combination. The experimental work showed that there was an increase in recovery for all of the cores with different percentages when compared to the initial case of just waterflooding if the vibration parameters are optimized. Full discussion of the results and recommendations for further investigations are discussed.
Abstract: In this paper the optimized vibro-seismic parameters for enhancing the productivity of the oil wells through water flooding is studied and presented. The main idea of the vibro-seismic application in the petroleum industry is to provide energy to the reservoir rock through the use of either physical vibration or acoustic waves vibrations to enhance...
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