In spite of new or old planning philosophies, log data preparation has been overlooked by a lot of players in the oil business. We are not speaking about conventional data preparation, such as depth shifting, normalization or editing, for these are very well taken care of in most projects. We want to go a step back. We are talking about the initial phase, even previous to vectorización. Well logs have a large variety of users but each of them has different needs and use different parts of the logs with different goals. Doing a gross generalization, Petrophysicists will go after most curves, use detailed-scaled logs (1:200), and concentrate in the reservoir area; geologists will usually go for SP, GR, resistivity and porosity logs in a reservoir-to-formation scale, using intermediate to large scale logs (1:500 and 1:1000); geophysicists mostly use large scales (1:1000 to 1:5000); and drillers use 1:10.000 or larger scales and mostly GR, density and sonic logs; and so on.

If the original digital data exists, every one will be happy, but if only analogical data exist, problems will rise because every specialist will try to get what he or she needs as fast as possible and with the quality that he or she needs without taking into account the needs of other specialists or the organization mid-to-long term needs. No one can blame them since there are deadlines and goals to achieve, but for the next project in the same area it will happen again….and again….and again. At the end the logs are digitized using completely different approaches leading to a chaotic mosaic of pieces of logs that do not match anyone's final needs, the data base becomes a mess and all upcoming projects will always have problems with log data.

In this work, a FRONT END LOADING approach is proposed and applied, leading to a group of specifications and best practices to be used as guidelines in well log preparation for Reservoir/Field/Asset/Corporation well log databases. The application of these guidelines will satisfy the needs of any well log user, optimize database management and improve the productivity of project teams. These best practices are supported by years of time-saving and technical improvements in a large numbers of projects. Time and money invested at the beginning of the projects will become even larger time and money savings at the long run.

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