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Using Different Methods to Predict Oil in Place in Mishrif Formation / Amara Oil Field
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The reserve estimation process is continuous during the life of the field due to risk and inaccuracy that are considered an endemic problem thereby must be studied. Furthermore, the truth and properly defined hydrocarbon content can be identified just only at the field depletion. As a result, reserve estimation challenge is a function of time and available data. Reserve estimation can be divided into five types: analogy, volumetric, decline curve analysis, material balance and reservoir simulation, each of them differs from another to the kind of data required. The choice of the suitable and appropriate method relies on reservoir maturity, heterogeneity in the reservoir and data acquisition required. In this research, three types of reserve estimation used for the Mishrif formation / Amara oil field volumetric approach in mathematic formula (deterministic side) and Monte Carlo Simulation technique (probabilistic side), material balance equation identified by MBAL software and reservoir simulation adopted by  Petrel software geological model.  The results from these three methods were applied by the volumetric method in the deterministic side equal to (2.25 MMMSTB) and probabilistic side equal to (1.24, 2.22, 3.55) MMMSTB P90, P50, P10 respectively. OOIP was determined by MBAL software equal to (2.82 MMMSTB). Finally, the volume calculation of OOIP by using the petrel static model was (1.92 MMMSTB). The percentage error between material balance and the volumetric equation was equal to 20% while the percentage error between the volumetric method and petrel software was 17%.

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Publication Date
Thu Jun 30 2022
Journal Name
Journal Of Economics And Administrative Sciences
Bayes Analysis for the Scale Parameter of Gompertz Distribution
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In this paper, we investigate the behavior of the bayes estimators, for the scale parameter of the Gompertz distribution under two different loss functions such as, the squared error loss function, the exponential loss function (proposed), based different double prior distributions represented as erlang with inverse levy prior, erlang with non-informative prior, inverse levy with non-informative prior and erlang with chi-square prior.

The simulation method was fulfilled to obtain the results, including the estimated values and the mean square error (MSE) for the scale parameter of the Gompertz distribution, for different cases for the scale parameter of the Gompertz distr

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