Predicting the effects of selected reservoir petrophysical properties on bottomhole pressure via three computational intelligence techniques

This study investigates the effects of selected petrophysical properties on predicting flowing well bottomhole pressure. To efficiently situate the essence of this investigation, genetic, imperialist competitive and whale optimization algorithms were used in predicting the bottomhole pressure of a reservoir using production data and some selected petrophysical properties as independent input variables. A total of 15,633 data sets were collected from Volvo field in Norway, and after screening the data, a total of 9161 data sets were used to develop apt computational intelligence models. The data were randomly divided into three different groups: training, validation, and testing data. Two case scenarios were considered in this study. The first scenario involved the prediction of flowing bottomhole pressure using only eleven independent variables, while the second scenario bothered on the prediction of the same flowing bottomhole pressure using the same independent variables and two selected petrophysical properties (porosity and permeability). Each of the two scenarios involved as implied in the first scenario, the use of three (3) heuristic search optimizers to determine optimal model architectures. The optimizers were allowed to choose the optimal number of layers (between 1 and 10), the optimal number of nodal points (between 10 and 100) for each layer and the optimal learning rate required per task/operation. the results, showed that the models were able to learn the problems well with the learning rate fixed from 0.001 to 0.0001, although this became successively slower as the leaning rate decreased. With the chosen model configuration, the results suggest that a moderate learning rate of 0.0001 results in good model performance on the trained and tested data sets. Comparing the three heuristic search optimizers based on minimum MSE, RMSE, MAE and highest coefficient of determination (R2) for the actual and predicted values, shows that the imperialist competitive algorithm optimizer predicted the flowing bottomhole pressure most accurately relative to the genetic and whale optimization algorithm optimizers

Application of artificial intelligence in predicting the dynamics of bottom hole pressure for under-balanced drilling: Extra tree compared with feed forward neural network model

This study used six fields data alongside correlation heat map to evaluate the field parameters that affect the accuracy of bottom hole pressure (BHP) estimation. The six oil field data were acquired using measurement while drilling device to collect surface measurements of the downhole pressure data while drilling. For the two case studies, measured field data of the wellbore filled with gasified mud system was utilized, and the wellbores were drilled using rotary jointed drill strings. Extremely Randomized Tree and feed forward neural network algorithms were used to develop models that can predict with high accuracy, BHP from measured field data. For modeling purpose, an extensive data from six fields was used, and the proposed model was further validated with two data from two new fields. The gathered data encompasses a variety of well data, general information/data, depths, hole size, and depths. The developed model was compared with data obtained from two new fields based on its capability, stability and accuracy. The result and model’s performance from the error analysis revealed that the two proposed Extra Tree and Feed Forward models replicate the bottom hole pressure data with R2 greater than 0.9. The high values of R2 for the two models suggest the relative reliability of the modelling techniques. The magnitudes of mean squared error and mean absolute percentage error for the predicted BHPs from both models range from 0.33 to 0.34 and 2.02%–2.14%, for the Extra tree model and 0.40–0.41 and 3.90%–3.99% for Feed Forward model respectively; the least errors were recorded for the Extra Tree model. Also, the mean absolute error of the Extra Tree model for both fields (9.13–10.39 psi) are lower than that of the Feed Forward model (10.98–11 psi), thus showing the higher precision of the Extra Tree model relative to the Feed Forward model. Literature has shown that underbalanced operation does not guarantee the improvement of horizontal well’s extension ability, because it mainly depends on the relationship between the bottomhole pressure and its corresponding critical point. Thus, the application of this study proposed models for predicting bottomhole pressure trends

Evaluation of influential parameters for supersonic dehydration of natural gas: Machine learning approach

The supersonic dehydration of natural gas is gaining more attention due to its numerous advantages over the conventional natural gas dehydration technologies. However, supersonic separators have seen minimal field applications despite the multiple benefits over other gas dehydration techniques. This has been mostly attributed to the uncertainty in ascertaining the design and operating parameters that should be monitored to ensure optimum dehydration of the supersonic separation device. In this study, the decision tree machine learning model is employed in investigating the effects of design and operating parameters (inlet and outlet pressures, nozzle length, throat diameter, and pressure loss ratio) on the supersonic separator performance during dehydration of natural gas. The model results show that the significant parameters influencing the shock wave location are the pressure loss ratio and nozzle length. The former was found to have the most significant effect on the dew point depression. The dehydration efficiency is mainly dependent on the pressure loss ratio, nozzle throat diameter, and the nozzle length. Comparing the machine learning model-accuracy with a 1-D iterative model, the machine learning model outperformed the 1-D iterative model with a lower mean average percentage error (MAPE) of 5.98 relative to 15.44 as obtained for the 1-D model

In-situ remediation of petroleumcontaminated soil by application of plantbased surfactants toward preventing environmental degradation

Remediation in this study employs the use of green plants and their extracts in enhancing the remediation process of polluted soils. GC-MS & FTIR techniques were employed in determining the constituents of the soil during the investigation. 60 ml of the extracts were applied on 1 by 2 ft segments of hydrocarbon polluted site and observed for two months. The results show that plant extract A significantly reduced the TPHs and PAHs to 5,450 and 126.2 mg/kg, respectively, as compared to those of extract B whose TPH and PAH values are 10,432 and 362.3 mg/kg, respectively. Both plant extracts reduced the total petroleum hydrocarbon compounds significantly when compared to the standard reference PAH and PAHs (4,500 mg/kg and 50 mg/kg respectively). The microbial plate count for the three media shows that the plant based surfactant had a synergy with the identified bacteria in enhancing Phytoremediation of the crude oil polluted site. Novelty statement: This study examined the application of two plant-based surfactants for remediation. These natural surfactants significantly reduced the petroleum hydrocarbon compounds present in the soil within the in-situ observation window. These Herbaceous plant family extracts have a great advantage as an eco-friendly alternative to synthetic surfactants, and they also exhibited an anti-fungi characteristic. The two biodegradable plant-based surfactants also significantly reduced the time that it could have taken for a remediation process

Numerical Based Optimization for Natural Gas Dehydration and Glycol Regeneration

Exergy is a simultaneous measure of the quantity and quality of energy. This helps to identify the inefficiency of the process and allows engineers to determine the cause and magnitude of the loss for each operating unit. Natural gas dehydration via absorption using glycol is the most economically attractive approach, and this advantage can only stand if lower energy consuption relative to adsorption process can be obtained; thus, timely prediction and identification of energy consumption is vital. In this study, an energy utilization predictive model for natural gas dehydration unit energy consumption was developed. This numeric approach will increase accuracy and reduce the high simulation time often encountered in using other simulation software. To achieve this novel idea, a multilayer perceptron approach which is a deep learning neural network model built on python using Tensorflow was adopted. The model used for this study is implemented to further increase the accuracy of the output set variables which are matched with simulation result. Since we are dealing with a non-linear function, rectified linear unit (ReLU) function was used to activate the neurons in hidden layers so as to strengthen the model to be more flexible in finding relationships which are arbitrary in the input parameter. These input parameters are fed into the steady state model and sent to various branches of fully connected neural network models using a linear activation function. Each branch produces a result for each output parameter thereby fitting the model by reducing the mean squared error loss. The training data were not normalized but left in their original form. Results showed that the adopted double hidden layer with 5 branches are uniquely branched in such a way that it predicts values for a single output variable, which is an upgrade to the former work done with a single hidden layer in literature. The accuracy analysis showed that the proposed double hidden layer approach in this study out-performed the single hidden layer

Pyogenic Liver Abscess Caused by Methicillin-Susceptible Staphylococcus aureus in a 21-Year-Old Male

Liver abscesses are the most common types of visceral abscesses. Pyogenic liver abscesses, a particular type of liver abscesses, are uncommonly encountered. We present a rare case of pyogenic liver abscess caused by methicillin-susceptible Staphylococcus aureus in a young man. A 21-year- old man presented from prison to the hospital with fever, nausea, vomiting, diarrhea, and abdominal pain for five days. Labs were significant for leukocytosis with predominant neutrophilia and elevated liver enzymes. CT abdomen with contrast revealed an 8.4 cm multiloculated right hepatic mass extending to the kidney. Patient was started on broad spectrum antibiotics, given septic presentation. Peripheral blood cultures returned positive for methicillin-susceptible Staphylococcus aureus (MSSA). The culture from percutaneous drainage also revealed MSSA. He received a total of four weeks of IV Nafcillin therapy along with drainage of his abscess via percutaneous catheter. Follow-up revealed clinical resolution. This case highlights the importance of obtaining an aspirate from the liver abscess to better guide treatment strategy. Clinicians must consider broadening antibiotic coverage to include gram-positive organisms if the patient presents with severe illness and risk factors for Staphylococcus aureus infections

Gastrointestinal Bleeding Secondary to Portal Hypertensive Duodenopathy in a Patient with Decompensated Liver Cirrhosis

With alcoholic cirrhosis and nonalcoholic fatty liver disease continuously on the rise in the United States, there is also a corresponding rise in portal hypertension. Portal hypertensive duodenopathy (PHD) is a complication of portal hypertension not commonly seen in cirrhotic patients. We present a case of a 46-year-old man who presented with decompensated liver cirrhosis secondary to gastrointestinal bleed. The patient underwent esophagogastroduodenoscopy (EGD) with findings indicative of PHD. Patient subsequently underwent transjugular intrahepatic portosystemic shunt (TIPS) with resolution of gastrointestinal bleed. We highlight TIPS as a management strategy in patients with PHD for whom less invasive measures are not effective

Liver Cholestasis Secondary to Syphilis in an Immunocompetent Patient

Liver involvement is a known feature of secondary syphilis. The prevalence of hepatitis in secondary syphilis ranges broadly from 1 to 50%. We report a case of a 37-year-old man with type 1 diabetes mellitus and sickle cell trait presenting with jaundice and acute liver cholestasis. Abdominal ultrasound revealed mild hepatic fatty infiltration. RPR and Treponema pallidum IgG results were positive with a reflex titer of 1:64. Liver biopsy revealed chronic hepatitis with normal hepatic architecture, Kupffer cell hyperplasia, hepatic cholestasis, and ductal proliferation suggestive of syphilitic hepatitis

Chronic Cannabis Intoxication and Propofol-Induced Salivation: Causes and Considerations

Legalization/decriminalization of cannabis will increase the numbers of patients who have had recent exposure to recreational or medical cannabis. Currently, little has been reported about potential interactions between cannabis use and Propofol anesthesia e.g., for oropharyngeal procedures. We describe three cases of ‘cannabis-induced hypersalivation after propofol’ (CHAP) and present our institutions’ experience with this unique pharmacological combination. Increased hypersalivation may complicate procedures and represent a procedural risk of suffocation. We evaluate possible pharmacological interactions that might underlie this phenomenon and consider management options going forward