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Ordinary and partial differential equations play a significant role across various energy domain as they aid in approximating solution for complex mathematical problems. Drilling optimization and reservoir simulation are some common application that takes the form of differential equations and are dominated by their respective governing equations. Approximating the solution of such mathematical problems requires a fast and reliable methodology. However, the computational complexity increases with the dimension for the classical numerical techniques and the quality of the result is dependent upon the discretization and sampling methods of the subspace. Recent advances in deep learning techniques, based on universal approximation theorem of neural network seems promising to tackle the high dimensional problem. The solution provided by deep learning for a differential equation is in a closed analytical form which is differentiable and could be used in any subsequent computation. In the present study, the solution for the initial condition and boundary value problems in ordinary and partial differential equation by deep learning method have been analyzed. The propsed algorithm could be valuable aid for analyzing the fluid flow and reservoir simulation in an effective manner.

Discharge of produced water (PW) and drilling wastes from oil and gas industry has a major impact to the flora and fauna. The purpose of this study is to investigate the PW impact on the Mediterranean region and to provide alternative ways to detect the harmful parameters present in the produced water and drilling waste. Study shows the PW and drilling waste analysis in lab and it shows the idea of using smart phone to know all the harmful parameters. Varies sensors are connected to water to detect the harmful parameters and these sensors are connected to Arduino and data can be viewed on the smart phone through things speak App. PW is produced in large amounts and has a multifaceted composition, having various toxic organic and inorganic mixes. Currently PW is treated in conventional ways such as multiphase separators, cyclones, and coarse filters to meets the existing regulation for discharge. Therefore, these treatment ways do not achieve more restrictive limitations to dispose or reuse the PW. Drilling waste is composed of Polynuclear Aromatic Hydrocarbon (PAH), Aliphatic Hydrocarbon, and heavy metals such as barium, chromium, cadmium, lead, strontium, mercury, lead, zinc, manganese, arsenic, copper, and Iron which are toxic to the surrounding environment. Toxic materials, which can threat the environment, such as Sulphide reducing microorganism and bentonite and barite reduce plant growth. The toxicity of PW waste can be decreased when properly treated before disposing into the surrounding environment. Studies in some area of Mediterranean region have displayed high level of some heavy metals associated with PW and drilling waste with concentrations higher than world health organization (WHO) standard; these have harmful impact on the regional environment.

Electrical conductivity measurements have been widely used in characterizing the cementitious materials. In mature stages, the influence of interface conductivity to the overall conduction was relatively small. However, its contribution during early hydration is still questionable. This paper calculated the interface conductivity during the first 24 hrs of hydration at elevated temperature and pressure up to 65°C and 3000 psi. The effect of elevated temperature to conductivity measurement is successfully corrected. Johnson equation is employed to predict the interface conductivity of pore-solid particles. The microstructural parameters that are used in the equation are estimated from the proposed particle expansion model. These calculation outcomes have a good agreement compared to the MIP measurements. The results showed that the interface conductivity grew slightly with the progress of drying. Its contribution to the bulk conductivity is relatively very small of about factor 6 in orders of magnitude. Hence, the influence of interface conductivity to the overall conduction might be disregarded during cement's early hydration.

The inter-connected pores of cement after displaced into wellbore is believed to contribute for fluids migration. It cannot be accurately evaluated using the present sonic and acoustic methods due to its density based principles. Impedance technique is applied at elevated temperature and pressure for its pores characterization. A simple particle expansion model is proposed to solve the microstructural parameters (surface and volume of pores) in the unsetting cement that was previously encountered. The proposed model is validated by comparing the trend lines of cement prior to (our model) and after (measurement) setting. The results are employed in the Katz-Thompson equation for estimating its permeability. A comparable result of permeability calculation to the measured data is fairly observed.

Electrical conductivity has been increasingly used for material characterization in the form of dispersion characteristic. However, the application of this method to the cement slurry is limited. This paper investigates the influence of water to cement ratio and degree of hydration to the dispersion characteristic of cement slurry during 24 hrs of hydration. The magnitude and frequency effect are used to describe the conductivity dispersion characteristic. It is observed that the magnitude of conductivity dispersion increases with the reduction in water content and pore volume. During initial hydration, the dispersion characteristic appears in the minimum value and raises as hydration continue. The frequency effect is growth as the hydration and saline concentration increases. It is implied that the single frequency measurement for evaluating the cement slurries properties may not be reliable. An implicit correlation to the interface conductivity is indicated from dispersion respond throughout hydration process.