New Models for Predicting Pore Pressure and Fracture Pressure while Drilling in Mixed Lithologies Using Artificial Neural Networks.

Precise prediction of pore pressure and fracture pressure is a crucial aspect of petroleum engineering. The awareness of both fracture pressure and pore pressure is essential to control the well. It helps in the elimination of the problems related to drilling, waterflooding project, and hydraulic fracturing job such as fluid loss, kick, differential sticking, and blowout. Avoiding these problems enhances the performance and reduces the cost of operation. Several researchers proposed many models for predicting pore and fracture pressures using well log information, rock strength properties, or drilling data. However, some of these models are limited to one type of lithology such as clean and compacted shale formation, applicable only for the pressure generated by under compaction, and some of them cannot be used in unloading formations. Recently, artificial intelligence techniques showed a great performance in petroleum engineering applications. Hence, in this paper, two artificial neural network models are developed to estimate both pore pressure and fracture pressure through the use of 2820 data sets obtained from drilling data in mixed lithologies of sandstone, carbonate, and shale. The proposed artificial neural network (ANN) models achieved accurate estimation of pore and fracture pressures, where the coefficients of determination (R 2) for pore and fracture pressures are 0.974 and 0.998, respectively. Another data set from the Middle East was used to validate the developed models. The models estimated the pore and fracture pressures with high R 2 values of 0.90 and 0.99, respectively. This work demonstrates the validity and reliability of the developed models to calculate pore and fracture pressures from real-time surface drilling parameters by considering the formation type to overcome the limitation of previous models.

Prediction of the Rheological Properties of Invert Emulsion Mud Using an Artificial Neural Network.

Successful drilling operations require optimum well planning to overcome the challenges associated with geological and environmental constraints. One of the main well design programs is the mud program, which plays a crucial role in each drilling operation. Researchers focus on modeling the rheological properties of the drilling fluid seeking for accurate and real-time predictions that confirm its crucial potential as a research point. However, only substantial studies have real impact on the literature. Several AI-based models have been proposed for estimating mud rheological properties. However, most of them suffer from non-being field applicable attractive due to using non-readily field parameters as input variables. Some other studies have not provided a comprehensive description of the model to replicate or reproduce results using other datasets. In this study, two novel robust artificial neural network (ANN) models for estimating invert emulsion mud plastic viscosity and yield point have been developed using actual field data based on 407 datasets. These datasets include mud plastic viscosity (PV), yield point (YP), mud temperature (T), marsh funnel viscosity (MF), and solid content. The mathematical base of each model has been provided to provide a clear means for models' replicability. Results of the evaluation criteria depicted the outstanding performance and consistency of the proposed models over extant ANN models and empirical correlations. Statistical evaluation revealed that the plastic viscosity ANN model has a coefficient of determination (R 2) of 98.82%, a root-mean-square error (RMSE) of 1.37, an average relative error (ARE) of 0.12, and an absolute average relative error of 2.69, while for yield point, this model has a coefficient of determination (R 2) of 94%, a root-mean-square error (RMSE) of 0.76, an average relative error (ARE) of -0.67, and an absolute average relative error of 3.18.

Synthesis, Characterization and Cytotoxicity Evaluation of Some Novel Pyridine Derivatives.

Reaction of isonicotinaldehyde with 2-cyanoacetohydrazide afforded (E)-2-cyano-N'-(pyridin-4-ylmethylene)acetohydrazide (1). Compound 1 was used as the precursor for the synthesis of novel pyridine derivatives by reaction with different arylidene malononitriles, malononitrile and acetylacetone to give pyridine derivatives 5a-e, 6 and 7, respectively. 4,4'-Bipyridine derivatives 9a-d were synthesized by a three-component reaction of isonicotinaldehyde, 2-cyanoacetohydrazide and activated nitriles 8a-d. Treatment of compound 9a with different aromatic aldehydes gave [1,2,4]triazolo[1,5-a]pyridine derivatives 11a-c. All reaction products were characterized by analytical and spectral data. For the novel compounds their bioactivity as antitumor agents was examined for in vitro cytotoxicity against HepG-2 and MCF-7. It was found that compounds 9a and 9b have high cytotoxic activity against both HepG-2 and MCF-7.

Tricuspid Valve Replacement in Infants and Children With Exclusively Autologous Tissue.

Tricuspid valve replacement has always been a challenge in the pediatric population, with high rates of mortality and morbidity. This article describes a new technique that we have used to replace the tricuspid valve with exclusively autologous tissues. The pulmonary autograft is used for tricuspid valve replacement. Pulmonary artery wall and autologous pericardium are utilized for right ventricular out flow tract reconstruction with the creation of a monocusp.

Minimally invasive right posterior minithoracotomy for open-heart procedures.

A right posterior minithoracotomy was evaluated in 123 selected patients between November 2002 and August 2006. Their ages ranged from 1.5 to 32 years (mean, 7.8 years) and weights ranged from 12.3 to 61.6 kg (mean, 23.3 kg). Pathology included atrial septal defect in 81 (66%), ventricular septal defect in 16 (13%), and 24 other (mainly valve) defects. All patients had a strictly posterior right minithoracotomy through the 4(th) or 5(th) right intercostal space, with a 7-9-cm skin incision. There was no mortality or procedure-related morbidity. The mean cardiopulmonary bypass time was 68 min, ischemic time was 47 min, and 47 (38%) patients were extubated on the operating table. The mean hospital stay was 4.3 days and it was < 5 days in 108 (88%) patients. A cosmetically fine scar was achieved in all patients. The right posterior minithoracotomy is a safe, cosmetically superior, and cost-effective approach for selected open-heart procedures.