Three-dimensional reservoir models are used routinely for various purposes in the E&P business to support value-based decisions. One of the key challenges in 3D reservoir modeling is distributing the identified facies and their associated properties in the defined 3D structural/stratigraphic framework respecting geologic knowledge and available well data. Different geostatistical techniques are used for populating the reservoir facies and properties in the 3D reservoir models which have different working inputs and assumptions. Most reservoir-characterization studies use variogram-based geostatistical-modeling methods to accurately and efficiently represent reservoir heterogeneities. The variogram-based techniques constrain 3D reservoir models to local data which represent the geologic knowledge and help to create appropriate flow behaviors through dynamic simulation. However, simulation results obtained from those techniques are highly dependent on available data, selected variogram model, and the geomodeler's geostatistical knowledge and geologic experience. To illustrate the impact of variogram modeling on 3D reservoir-modeling outcomes, multiple 3D reservoir models were generated using different variograms for a siliciclastic reservoir. As expected, the results obtained from the models show significant variations, which indicate that selection of appropriate variogram models, which is critical for facies and property distribution in 3D static models, affects original hydrocarbon in place (OHIP) and recoverable resources/reserves estimation and production forecasts. A sensitivity analysis of the variogram parameters in the 3D static models and its impact in the dynamic simulation should be considered an integral part of the 3D modeling workflow.

This paper discusses a new workflow to stochastically estimate the performance of infill locations in a mature oil or gas field. Usually performance evaluations for infill wells are conducted using either much generalized statistical methods or numerical simulation. Both approaches have a significant drawback; the prior being quick however very often lacking in accuracy, the latter being very accurate however usually very complex in setup and computation. The presented workflow is a new approach to infill well performance prediction that combines speed and reasonable accuracy. The workflow generates a set of key performance indicators of existing wells derived from historic dynamic data (fluid production rates, pressures, etc.), static data (reservoir properties, etc.) and predicted data (simplified production forecasts). The wells are then grouped according to the similarity of their KPIs. The production profiles of the wells within the same group are combined to a type curve that is described by the most likely production profile and an associated uncertainty range. A data-driven expert system is used to identify and capture the correlations of the parameters such as geographic locations, well spacing, reservoir properties and the group membership (equivalent to type curve). This expert system can then be applied to any location in the field in order to determine the most likely group membership of a potential infill well. The classification of an infill well to a group is hereby not necessarily unique; the expert system might classify an infill well into several groups and assign a probability of occurrence for each of the groups. A Monte Carlo routine is then applied to forecast the performance of the infill locations honoring the respective probability of occurrence of each type curve. The presented approach has been successfully applied for infill well selection in a statistical field development study for YPF in the Argentinean San Jorge Basin. Introduction Despite the long production history with more than 20 000 wells, the current methodology to make decisions still takes a long and costly path consisting of testing, plugging and stimulation procedures. The authors believe that, in parallel to the reservoir modeling studies, YPF can make use of the immense array of well data and the wisdom acquired over one century of production history, to produce a set of innovative practices to boost the efficiency of their current operations. Therefore a statistical approach based on the power of emerging computing tools for data mining can assist YPF to recognize patterns and develop methodologies which are strong enough to tackle the aforementioned technical challenges, transforming these opportunities in real bottom line results. The goal of the study is to generate sound technical arguments to formulate an innovative strategy to accelerate the exploitation of oil and gas assets of YPF in the San Jorge Basin, Argentina. The workflows of interest areidentify candidates for infill drilling locations,propose a field development strategy based on lessons learned from the past in order to know the size of the business from an economic point of viewidentify benefits through optimizing infill locations using data mining methodology. The objective of this paper is to present the methodology and results obtained during the analysis phase as value promise for field development.

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Although the San Jorge and Neuquen Basins contain some of Argentina's oldest and most important oil and gas reservoirs, significant potential for infill and step-out drilling remains. The high degree of lateral and vertical heterogeneity within reservoir strata has traditionally made reservoir prediction difficult, while key structural features resulting from multiple phases of tectonic deformation are often subtle but complex, making detailed compartmentalization and fracture analyses uncertain. Nevertheless, a detailed geological characterization is required for the effective re-development of existing reservoirs, particularly when planning secondary recovery and infill drilling programs. Following the acquisition of large 3D seismic surveys in these basins, YPF sought to improve the predictability of reservoir quality and subtle structural elements through the use of seismic attributes. In many cases, the reactivation of pre-existing faults causes areduction of offset and structural inversion, presenting a definition problem seismically, particularly in areas of high rock velocity and low seismic frequency. In order to accurately define subtle structural features, we have interpreted 3D seismic curvature volumes covering the El Guadal Norte and Bateria 2 Fields of the San Jorge Basin, and the Bajo Del Piche, and Senal Cerro Bayo Fields of the Neuquen Basin. The seismic processing technique employed in creating the volumes, calculates the maximum correlation between each point in the seismic cube and its neighbors. The dip and direction represented by the maxima are assumed to represent structural orientation - similar to a three-dimensional dipmeter - so the magnitude and style of structural folding can be determined. Positive curvature represents anticlinal folding, and negative curvature represents synclinal folding. Faults are located in zones where there are parallel trends of positive and negative bands of curvature. Volumes of most positive curvature and most negative curvature are used to help define structural axes and likely locations for natural fracture systems. Integrating these attributes with engineering data has greatly improved our understanding of reservoir behavior, helping to explain why specific areas exhibit high or low productivity, high water cut, and compartmentalization. This will allow for more intelligent planning of secondary recovery programs, and targeting of infill or step-out drilling programs. To quantify the relationship between seismic response and reservoir quality, seismic modeling studies were undertaken in the El Guadal Norte and BaterÍa 2 fields of the San Jorge Basin. Wells containing sonic and density logs were used to create log interpolation seismic models, which were made as realistic as possible by rigorously tying them to actual 3D seismic data. This allowed for a better understanding of seismic amplitudes. More importantly, 3D acoustic inversion studies were performed to accurately define reservoir heterogeneity. The 3D impedance volume exhibits distinctly low acoustic impedance in areas known to contain volcanic tuffs which impede permeability. The impedance volume shows significant depositional detail, displaying fingering of the tuffs, and indicating areas where untapped reservoir sands reside. These undrilled sections are often in up-dip combination structural-stratigraphic trap configurations, opening new drilling opportunities.

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