fax 01-972-952-9435. AbstractDew point pressure is a crucial parameter for evaluation of gas condensate reservoirs. Hence, the availability and accuracy of this property has been the main bottleneck for the reservoir simulation and development. Although the dew point pressure can be determined experimentally from collected laboratory samples frequently these measurements are not available. In these cases, fluid reservoir properties are determined with the use of empirical correlations (explicit methods) or they can be determined iteratively using an equation of state (EOS). Theoretical results and practical experience indicate that feed forward network can approximate a wide class of function relationships very well. This property is exploited in modeling petroleum engineering process. In this work, a set of conventional feed forward multilayer neural network have been proposed to predict dew point pressure of gas condensate reservoirs. The accuracy of the method is evaluated by its application for dew point pressure estimation of various reservoir fluids not used in the development of the model. Furthermore, the performance of the model is compared against the performance of other alternatives correlations reported as the most accurate and general correlations for dew point pressure prediction. Results of this comparison show that the proposed method out performs the other alternatives, both in accuracy and generality. The network was developed using experimentally Constant Volume Depletion (CVD) measured condensate sample of south pars reservoir and collected data from literature of 111 gas condensate samples covering a wide rang of gas properties and reservoir temperature.The network has an average absolute error of 2.573%, 3.832% and 2.612% for training, validation and test processes, respectively.

Dew point pressure is a crucial parameter for evaluation of gas condensate reservoirs. Hence, the availability and accuracy of this property has been the main bottleneck for the reservoir simulation and development. Although the dew point pressure can be determined experimentally from collected laboratory samples frequently these measurements are not available. In these cases, fluid reservoir properties are determined with the use of empirical correlations (explicit methods) or they can be determined iteratively using an equation of state (EOS). Theoretical results and practical experience indicate that feed forward network can approximate a wide class of function relationships very well. This property is exploited in modeling petroleum engineering process. In this work, a set of conventional feed forward multilayer neural network have been proposed to predict dew point pressure of gas condensate reservoirs. The accuracy of the method is evaluated by its application for dew point pressure estimation of various reservoir fluids not used in the development of the model. Furthermore, the performance of the model is compared against the performance of other alternatives correlations reported as the most accurate and general correlations for dew point pressure prediction. Results of this comparison show that the proposed method out performs the other alternatives, both in accuracy and generality. The network was developed using experimentally Constant Volume Depletion (CVD) measured condensate sample of south pars reservoir and collected data from literature of 111 gas condensate samples covering a wide rang of gas properties and reservoir temperature. The network has an average absolute error of 2.573%, 3.832% and 2.612% for training, validation and test processes, respectively. 1. Introduction One of the crucial steps in gas reservoir evaluation and simulation is dew point pressure prediction of retrograde samples. Accurate reservoir fluid properties are essential for all reservoir-engineering calculations such as estimation of reserves and forecast and planning of future enhanced oil and gas production. Hence, the availability and accuracy of this property has been the main bottleneck for the gas reservoir simulation and development. Calculations show that even small error in estimation of PVT properties leads to a much higher error in reservoir's model design, operation and control. This small error results in inappropriate reservoir simulation and poor performance of the reservoir's model. In general, there are two classes of estimation methods for calculation of dew point pressure. The first class is determined experimentally from collected laboratory samples frequently these measurements are not available. The second class, fluid reservoir properties are determined with the use of empirical correlations (explicit methods) or they can be determined iteratively using an equation of state (EOS). Empirical correlations are relatively easy to use, but to date, they have not been able to duplicate reliably the temperature behavior of constant-composition fluids. An EOS, while duplicating the behavior of constant-composition fluids, may have convergence problems and must be calibrated to existing and available experimental data. The statistical approach is comparatively a more versatile approach to the problem of PVT parameters prediction. It makes use of the available experimental dew point data (the dependent variable) and develops functional relationships with the experimental fluid characterization and reservoir temperature data (the independent variables). It, however, requires the assumption and satisfaction of multi-normal behavior and linearity, and hence it must be applied with caution.

During the past decades hundreds of wells have been constructed in the giant South Pars gas field with different approaches and methodologies leading to diverse outcomes and results. Interestingly enough there have been many operators in the field with distinct performances yet by deploying the same vendors and service providers held in common. By considering that it is statistically proven that the larger the project budget, there are higher chances for failure, effective tailoring of methodologies in large scale mega-projects would be crucial and decisive. In many cases failures are a consequence of simply applying methodologies already experienced in small scale projects on complex and sophisticated projects with a variety of influential factors and a huge network of stakeholders. The study has provided an insight on how effective approaches in project management or conversely mismanagement could play as the main rout cause in a chain of events corresponding to either saved time or Non-Productive Times in well construction operations by presenting real cases elaborated in detail. Cases show how failure in collecting requirements and recognizing the interdependencies among each necessity in the early stages of the project and subsequently overlooking the related cost, time and risks could cause massive cumulative financial loss. Additionally, failure to recognize wise investments in vendors and service providers as the 1-10-100 rule which explains how failure to take notice of one initial investment escalates the financial loss exponentially would have staggering consequences. The current paper explains how being proactive in an extensive planning phase could ease the execution stage as a great investment in time spent. From a human resource perspective, it has been demonstrated that how the ability and skills of the project team e.g. effective communication in complex networks with multiple reporting relationships and data-driven decisions by statistics could prevent cognitive biases and errors in decision making. Furthermore, powerful alignments come from shared and common motivations and morale for all engaged parties and the supply chain being service providers, rig contractors or the field operator and there should be an answer to the question, what's in it for me when asked by each of these parties. Overall the study presents real cases and lessons learned showing how the main rout causes of Non-Productive Times could go back deeply to ineffective project approaches from a project management perspective in a system dynamic chain of events rather than to just address the emerged symptoms of failure in drilling operation.