No abstract

Barite is one of the most common weighting materials used in drilling fluid for deep oil and gas wells. Consequently, the main source of solids building the filter cake is the weighting material used in drilling fluids ‘Barite particles’. Barite is insoluble in water and acids such as HCl, formic, citric, and acetic acids, as well as the barite has low solubility in chelating agent such as Ethylene Diamine Tetra acetic Acid (EDTA) and Hydroxyethyl Ethylene Diamine Tri Acetic (HEDTA). The present study introduces a new formulation to dissolve the barite filter cake using two types of treatments. The removal treatment consiting EDTA or HEDTA plus converting catalyst. The removal formulation also contains polymer breaker (Enzyme). Solubility tests conducted using industrial barite particles with size ranged from 30 to 60 micron. The solubility experiments were carried at 200 °F for 24 hours. Different concentrations of catalyst were added to select the optimum concentration. The designed formulation was examined to remove filter cake formed by Barite drilling fluid using High Pressure High Temperature cell (HPHT). The result of this study showed that the barite filter removal efficiency of new formulation reached to 90 %. The solubility test results presented that the solubility of barite particles in 20 wt. % EDTA was 60 % in 24 hours at 200 °F. Adding one of the catalyst the solution increased the solubility of barite to 85 %. The second treatment with HEDTA perform 60% removal efficiency in the presence of catalyst. The used treatments were not compatible with the polymer breaker (Enzyme) so the filter cake removal must be two stages. The first stage using 10 wt. % enzyme for 24 hours to break the polymer and the second stage using the designed barite formulation. The investigated formulation that effectively dissolve the barite will change the industrial direction to use the barite as weighting agent because dissolving barite was the most significant limitation of using barite in the drilling fluid.

The drilling mud program contains many tests such as filtration rate and filter cake properties to select the proper drilling fluid additives that yield the standard ranges of the viscosity, filtration rate, etc. However, the physical and chemical changes in the mud composition during the mud circulating will cause changes to the filter cake properties. The changes in the filter cake properties should be considered in the mud design program to prevent the problems associated with the change in the drilling fluid properties. For long horizontal wellbores penetrating plastic formations, the two sources of solids in filter cake are drilling chemical additives and formation cuttings (sand particles in the case of sandstone reservoir). This study focuses on the effect of introducing sand particles from the drilled—formations on the filter cake properties. Real drilling fluid samples from the field were collected at different location during drilling a 3600 ft of the horizontal section of a sandstone formation. Calcium Carbonate (CaCO3) was used as weighting material in this filed. The drilling fluid samples were collected at two different points: the flow line coming from the well after shale shaker and the flow line going to the well to verify the effect of separation stages on filter cake properties. The primary drilling fluid properties of the collected samples were measured such as density and rheological parameters. High pressure high temperature (HPHT) filter press was used to perform the filtration and filter cake experiments at 300 psi differential pressure and room temperature (25 °C). The mineralogy of the external filter cake formed by fluid loss cell is determined using SEM (scanning electron microscopy) and XRD (X-ray diffraction). Finally, solubility test was conducted to evaluate the effect of sand particles on filter cake removal (containing Calcium Carbonate as weighting material) using chelating agent: glutamic diacetic acid (GLDA) at pH 4. The results showed that for long horizontal sections, the effect of introducing sand particles to the composition of the filter cake can cause significant change to the properties of filter cake such as mineralogy, thickness, porosity, and permeability. For instant the thickness of filter cake increased about 40% of its original thickness when drilling sandstone formation in horizontal well due to fine sand particle settling. The filter cake porosity and permeability increment in the first 2000 ft part of the horizontal section was observed clearly due to the irregular shape of the drilling particles. However for the points after the first 2000 ft of horizontal lateral, the porosity and permeability almost remained constant. Increasing the sand content up to 20% degrade the dissolution rate of calcium carbonate in the GLDA (pH = 3.8) to 80% instead of 100%.

Crude oil viscosity is a significant parameter for the fluid flow in both porous media and pipe lines. Therefore, it has to be determined using highly accurate methods. Oil viscosity is usually predicted with the correlations obtained from the laboratory measured data. However, some of the presented correlations have very complicated assumptions which make them very difficult to apply in most of the case studies reported. On the other hand, simplified correlations companies the accuracy. The present work in this paper studies predictive capabilities of Artificial Intelligence (AI) to estimate the oil viscosity. Artificial Neural Network (ANN) models are proposed to predict the undersaturated, saturated and dead oil viscosity in Yemeni fields. A data set consisting 545 of laboratory measurements on oil samples was gathered from different oil fields in Yemen. 70% of the data points were used to train the proposed ANN models while the remaining data set was tested the model performance. The performance of the ANN methods was compared with some of the conventional correlations such as (Beal's correlation, Khan's correlation, Kartoatmodjo and Schmidt correlation, Vasquez-Begg's correlation, Chew and Connaly correlation, Beggs and Robinson correlation, Elsharqawy correlation and Glaso's correlation). The result of this study shows the superiority of the Artificial Neural Network (ANN) models over the current models for predicting oil viscosity from PVT data. The comparative results displayed that the proposed ANN models performed better with higher accuracy than those obtained with published correlations.