The missing data can be a major challenge for any researcher who needs the accuracy and reliability of his statistical studies. In any statistical study, missing data, regardless of number, can cause a dramatic shrink of sample size and, because of that, a nonreliable statistical conclusion. Due to this problem, the accuracy of any statistical method applied, such as estimates of population parameters, and statistical applications will be impaired and statistical power will be weaker. All researchers will encounter this problem eventually, during the empirical research process, and it will be up to them to evaluate how they will address the missing data. The missing data can be caused by many di erent reasons; they are classi ed as missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR). In the oil industry, in the process of oil and gas exploration or production, the missing data can be caused due to di erent reasons and they can be of any type of classi cation mentioned before. As a result of missing data from an oil eld, the matrix of geological data consisting of parameters values such as density, permeability, oil saturation, temperature, pressure, etc., will a ect the oil eld geological estimates, which will be re ected in the oil eld reserves estimation, the oil eld production performance evaluation, etc. The assessment of the type of missing data is very important as well as nding and implementing the best method of the imputation of lling in the missing data. We will analyze and apply several methods of missing data imputation and evaluate the impact on reserves estimation of the M/D oil eld in Albania.