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Résumé -Nouveau modèle pour l'optimisation de la conception 3D des puits -Ce travail utilise un logiciel se servant d'un algorithme génétique pour déterminer la profondeur optimale de puits directionnels et horizontaux dans un espace 3D. Nous utilisons une fonction spécifique de pénalité, la mutation, les probabilités croisées et un critère de terminaison pour obtenir le minimum global de profondeur de forage. Ce minimum est atteint aux valeurs minimales du point de démarrage, de l'angle de forage et des taux d'augmentation et de déclin de production. Les valeurs minimales de ces paramètres amoindrissent la sévérité des virages, entraînant une réduction des problèmes en opération. La conception optimisée est comparée pour deux puits à la conception conventionnelle (à base d'une méthode d'essai et d'erreur) et au logiciel WELLDES (à base d'une technique de minimisation séquentielle sans contraintes). La conception optimisée permet de réduire la longueur totale de forage des deux puits, tout en maintenant les autres paramètres opérationnels dans les contraintes imposées. Dans la conception conventionnelle et le logiciel WELLDES, quelques variables sortent de leurs limites de contrainte. Oil & Gas Science and Technology -Rev. IFP, Vol. 59 (2004) total drilling measured depth, m T dogleg severity, °/30.48 m ANG1U upper limit on inclination angle after first build section, m ANG2U upper limit on inclination angle after drop section, m ANG3U upper limit on inclination angle after second build section, m ANG1L lower limit on inclination angle after first build, m ANG2L lower limit on inclination angle after drop section, m ANG3L lower limit on inclination angle after second build section, m DKMAX upper limit on depth of kickoff point, m DKMIN lower limit on depth of kickoff point, m CASD1U upper limit on casing setting depth after first build, m CASD2U upper limit on casing setting depth after drop section, m CASD3U upper limit on casing setting depth after second build section, m CASD1L lower limit on casing setting depth after first build, m CASD2L lower limit on casing setting depth after drop section, m CASD3L lower limit on casing setting depth after second build section, m D v2 true vertical depth after at end of first hold section, m D v3 true vertical depth after at end of drop section, m AANG1U upper limit on azimuth angle at kickoff point, degrees AANG2U upper limit on azimuth angle after first build, degrees AANG3U upper limit on azimuth angle after first hold section, degrees AANG4U upper limit on azimuth angle after drop section, degrees AANG5U upper limit on azimuth angle after second hold section, degrees AANG6U upper limit on azimuth angle after second build section, degrees AANG1L lower limit on azimuth angle at kickoff point, degrees AANG2L lower limit on azimuth angle after first build, degrees AANG3L lower limit on azimuth angle after first hold section, degrees AANG4L lower limit on azimuth angle after drop section, degrees AANG5L lower limit on azimuth angle after second hold section, degrees AAN...

Successful prediction of the future performance of condensate reservoirs requires accurate values of dewpoint pressures. Although the dewpoint pressure can be measured experimentally from collected laboratory samples, these measurements are frequently not available. In these cases, dewpoint pressure is determined using empirical correlations or using an equation of state (EoS). This paper presents an application of genetic programming with the orthogonal least squares algorithm (GP-OLS) to generate a linear-in-parameters dewpoint pressure model represented by tree structures. The GP-OLS-based gas condensate reservoir dewpoint pressure model was generated as a function of reservoir fluid composition (in terms of mole fractions of methane through heptanes-plus, nitrogen, carbon dioxide, and hydrogen sulfide), molecular weight of the heptanes-plus fraction, and reservoir temperature. The new model was developed using experimental measurements of 245 gas condensate systems covering a wide range of gas properties and reservoir temperatures. A total of 135 gas condensate systems that had not been used in building the new model were used to test and validate the new model against the other early published correlations. The validity test shows that the new model has a lower average absolute relative error than other published correlations. Therefore, the new model can be considered an alternative method to estimate the dewpoint pressure when the experimental data are not available.

Permeability is one of the most important characteristics of hydrocarbon bearing formations. Formation permeability is often measured in the laboratory from reservoir core samples or evaluated from well test data. However, core analysis and well test data are usually only available from a few wells in a field. On the other hand, almost all wells are logged. This paper presents a non-parametric model to predict reservoir permeability from conventional well log data using an artificial neural network (ANN). The ANN technique is demonstrated by applying it to one of Saudi Arabia's oil fields. The field is the largest offshore oil field in the world and was deposited in a fluvial dominated deltaic environment. The use of conventional regression methods to predict permeability in this case was not successful. The ANN permeability prediction model was developed using some of the core permeability and well log data from three early development wells. The ANN model was built and trained from the well log data and their corresponding core measurements by using a back propagation neural network (BPNN). The resulting model was blind tested using data which was taken from the modelling process. The results of this study show that the ANN model permeability predictions are consistent with actual core data. It could be concluded that the ANN model is a powerful tool for permeability prediction from well log data. Introduction Many oil reservoirs have heterogeneity in rock properties. Understanding the form and spatial distribution of these heterogeneities is fundamental to the successful exploitation of these reservoirs. Permeability is one of the fundamental rock properties, which reflects the rock's ability to transmit fluids when subjected to pressure gradients. While this property is very important in reservoir engineering, there is no specific geophysical well log for permeability, and its determination from conventional log analysis is often unsatisfactory(1). In general, porosity and permeability are independent properties of a reservoir. However, permeability is low if porosity is disconnected, whereas permeability is high when porosity is interconnected and effective. Despite this observation, theoretical relationships between permeability and porosity have been formulated, such as the Kozeny-Carmen theory. The Kozeny-Carmen theory relates permeability to porosity and specific surface area of a porous rock which is treated as an idealized bundle of capillary tubes. This theory, however, ignores the influence of conical flow in the constrictions and expansions of the flow channels and treats the highly complex porous medium in a very simple manner. Empirical relationships based on the Kozeny-Carmen theory have also been developed that relate permeability to other logs and/or log-derived parameters such as resistivity and irreducible water saturation(2). These relationships are applied only to the region above the transition zone or to the transition zone itself. Since core permeability data are available for most exploration and development wells, statistical methods have become a more versatile alternative in solving the problem of determining reservoir permeability. Regression is widely used as a statistical method to search for relationships between core permeability and well log parameters(3, 4).

Wellbore instability resulted in the largest percentage of non-productive time (NPT) during drilling stage in oil industry. Wellbore instability during drilling includes: wellbore pack-off, excessive torque and drag, blowout, stuck pipe and other related well problems. Many studies assumed that wellbore instability problems were due to physical and chemical interactions between rocks and drilling fluid; and neglected impact of drillstring vibration on wellbore stability. Drillstring vibration is usually mitigated while drilling not for wellbore stability issues; but to prevent drillstring fatigue and downhole tools failure by not exceeding vibration operation limits for drillstring. This work aims to develop a new approach studies the impact of drillstring vibration on wellbore stability by analyzing different rock failure mechanisms can happen to wellbore due to drillstring vibration; and computing drillstring vibration limits (acceleration values) that can collapse wellbore. Rock failure mechanisms and drillstring dynamic behavior while vibration was studied to understand consequences happened due to drillstring collision with wellbore. Authors innovated a new model to predict drillstring vibration hotspots values that can collapse wellbore. Results show that drillstring vibration can collapse wellbore by three failure mechanisms: a- when drillstring vibration applies stresses above rock strength, rock compressive failure will take place; b- if drillstring vibration applies repeated cyclic loads on rock continuously; rock fatigue will occur; c- when cyclic loads on rock are not strong enough to cause rock failure; rock fatigue will reduce rock strength and lead to rock shear failure. Model computes drillstring acceleration values that can fail wellbore by these failure mechanisms; and calculates mud weight needed to prevent rock shear failure before and after reduction in rock strength due to rock fatigue. Model outputs must be compared with drillstring vibration operation limits; and the lower value must be used as a boundary limit for drillstring vibration while drilling to prevent wellbore collapse and drillstring failure. These will make model results used as an early detector for harmful drillstring vibration shocks on wellbore.