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Understanding and capturing the uncertainty of the reservoir are keys to predicting its performance and making operational decisions. Conventional industry practices with a single or three (high-mid-low case) models have little ability to describe the full complexity of subsurface uncertainty and often yield poor performance in forecasting. To improve our understanding of the effect of reservoir uncertainty in performance, we need to use an ensemble of models which spans the full space of the uncertain parameters. These parameters may range widely from global parameters, such as water-oil contact and fault transmissibility, to cell-based properties, such as heterogeneous permeability and porosity. While it is ideal to explore all the possible parameter combinations, doing so can easily result in millions of models and become impractical for history matching and forecasting purposes. In this work we present a two-step history matching workflow where the uncertainties in the local heterogeneity and global parameters are investigated sequentially and yet in a manageable manner. The workflow begins with a geological model built based upon available information. The local geological heterogeneity, which cannot be readily determined from the information such as seismic images or well logs, is examined in the first step of the workflow. We create an ensemble of 103 to 105 models which span the uncertainty space for properties like permeability, porosity and/or net-to-gross but all are constrained to geostatistical data such as ranges, standard deviations and variograms. To effectively reduce the ensemble size, we implement the dynamic fingerprinting technique, a method based on streamline information (time of flight or drainage time), to screen and cluster the models. The concept behind this methodology is that for each distinct property realization, a given production schedule will generate a flow pattern which, like a fingerprint, is unique to that realization. This method is highly efficient since the time required to obtain the characteristic flow pattern is significantly shorter than the time of interest (typically the whole production history). The fingerprints from individual realizations are collected and clustered according to their principal flow pattern through single value decomposition. Each cluster aggregates a set of model realizations that despite their apparent difference in the model space, all correspond to similar principal flow trends. A single representative is then chosen from each cluster. The second step of the workflow aims to examine the uncertainties of the global parameters. For each representative obtained from the first step, we implement the standard workflow for Design of Experiment and proxy modeling to construct response surfaces as functions of global parameters. Algorithms such as Markov Chain Monte Carlo are then implemented to perform vast sampling and condition the models to the history data. The end result is a small set of models that are based on realistic geology, preserve flow-relevant subsurface uncertainty, and are conditioned to production data. The proposed workflow, which can be referred to as the probability history matching (PHM) workflow, provides an efficient and effective way to select representatives and condition to historical data. The selected models can be used for making forecasts and support development planning under uncertainty. An application of this workflow is shown on a real-world field example.

The Macueta is a gas field located in the Acambuco Area, Tarija Basin, Northwest Argentina. This area is owned by the Acambuco Consortium: Pan American Energy (operator, 52%), Shell (22.5%), Repsol-YPF (22.5%), Apco (1.5%) and NorthWest (1.5%). The reservoir is formed by tight naturally fractured quarzitic sandstones of Devonian age (Huamampampa and Icla Formations), trapped in a narrow and elongated anticline within the subandean region. Since the early 80's, four drilling initiatives, including vertical and horizontal wells, showed moderate to no success at all (AOF = 3 MMscfd − 14 MMscfd). A new approach, using modern well testing and logging tools, was performed. This approach, including a careful study of the fracture system, in situ stress analysis and fracture production characterization, turned the view and triggered the field development. The key to success was the identification, through selective zone well testing, of a zone far from the wellbore with high permeability, and its association with better fracture development in the crestal zone of the structure. This finding helped to understand the production mechanism of the reservoir. This study is supported by acoustic image logs, production logs and well tests taken in Mac.x-1001bis, a former vertical well. In this well, a succesful drilling reentry operation was executed after planning a 1150 ft long horizontal well trajectory with improved chances of success by maximizing fracture intersection. The Mac.x-1001bis reentry well tested 36 MMscfd of gas and it has an absolute open flow (AOF) of 144 MMscfd. This increase of productivity, up to 40 times compared with the former wells, proves the convenience of horizontal technology in naturally fractured environments when it is carefully planned and executed. This well sets a record in the basin for the Icla Fm. and opens new expectations and possibilities for the development of this type of reservoirs. Introduction A highly successful experience which relates horizontal drilling, effective fracture identification and production deliverability is reported in a naturally fractured reservoir (NFR). A careful planning and selection among several drilling alternatives is presented. Although the existence of gas was proven since the early 80 s, the productivity per well was low and the field development has been delayed for several years. Table 1 summarizes the results and history of existing wells. The Mac.x-1001 (1982) well discovered the field in Huamampampa Fm. but due to technical reasons, it was abandoned. Mac.x-1002 well (1984) was planned as an offset well and was also abandoned due to the lack of productivity. The Mac.x-1001bis (2000), a twin well of the former Mac.x-1001, reached moderate levels of productivity, and provided new information acquired with modern technology.

Assisted history matching (AHM) of a channelized reservoir is still a very-challenging task because it is very difficult to gradually deform the discrete facies in an automated fashion, while preserving geological realism. In this paper, a pluri-principalcomponent-analysis (PCA) method, which supports PCA with a pluri-Gaussian model, is proposed to reconstruct geological and reservoir models with multiple facies. PCA extracts the major geological features from a large collection of training channelized models and generates gridblock-based properties and real-valued (i.e., noninteger-valued) facies. The real-valued facies are mapped to discrete facies indicators according to rock-type rules (RTRs) that determine the fraction of each facies and neighboring connections between different facies. Pluri-PCA preserves the main (or principal) features of both geological and geostatistical characteristics of the prior models. A new method is also proposed to automatically build the RTRs with an ensemble of training realizations. An AHM work flow is developed by integrating pluri-PCA with a derivative-free optimization algorithm. This work flow is validated on a synthetic model with four facies types and a real-field channelized model with three facies types, and it is applied to update both the facies model and the reservoir model by conditioning to production data and/or hard data. The models generated by pluri-PCA preserve the major geological/geostatistical descriptions of the original training models. This has great potential for practical applications in large-scale history matching and uncertainty quantification.

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