Mario A. Gutierrez

Calibration and ranking of pore-pressure prediction models

Pore-pressure prediction employs a broad range of methodologies to estimate fluid pressures including porosity and depth-based trends, seismic velocities, and multivariate regressions. Model building is typically characterized by an iterative sequence that includes model identification, calibration, selection, and diagnostic checking. To predict effective stress and pore pressure, practitioners apply a diverse set of functional forms that relate velocities and fluid pressures, using, in addition to seismic velocity, predictive variables like porosity, depth, temperature, etc.

On the universality of diagenetic trends

For decades, the main use of seismic data has been to delineate sedimentary bodies and tectonic features in the subsurface. The mission of seeing inside the geologic body has been added recently. Mapping porosity, lithology, and other reservoir bulk properties inside the geologic body has become possible due to the recent dramatic improvement in seismic acquisition, imaging, and inversion quality as well as the accompanying progress of rock physics.

Textural sorting effect on elastic velocities, part II: elasticity of a bimodal grain mixture.

Summary Here is presented a rock physics model that predicts the effect of textural sorting and variable composition and fabric (e.g. grain- and mud-supported fabric) on velocity and porosity. The model links porosity, mineralogy, pressure, texture, fabric, and pore fluid bulk modulus to the elastic rock properties. The theoretical relationships among seismic wave velocities, grain sorting, and composition suggested in the bimodal gain mixture model are essential when seismic or sonic data is used to predict reservoir quality in clastic rocks.

Theoretical rock physics for bypassed oil detection behind the casing: La Cira-Infantas oil field

La Cira-Infantas oil field (LCI) in the Middle Magdalena Valley Basin of Colombia has estimated original oil in place of 3700 million barrels. However, cumulative production of more than 1700 wells has not exceeded 724 million barrels. The recovery factor is about 20%. About 55% of LCI wells were drilled before the development of borehole logging. Dickey (1992) wrote of this area: “Before electric logging, it was impossible to see where the individual pay sands were by examination of the cuttings. Consequently, many sands were shut off behind pipe.” Thus, in mature oil fields like LCI with low recovery factor, well recompletion guided by new cased-hole logging is economically attractive because moderate investment may reveal bypassed oil-filled intervals.

Stratigraphy-guided rock physics

Porosity and permeability heterogeneity prevents efficient drainage and sweep of hydrocarbons and results in low recovery efficiency. This heterogeneity is often linked to the facies architecture inherited from the original depositional system as well as to subsequent rock diagenesis. Undrained zones can be precisely targeted if a detailed description of reservoir bulk properties in 3-D is available. It is natural to derive such description from 3-D reflection seismology because of its superior ability to illuminate the subsurface.

3-D seismic interpretation of tectonic wrenching and faulting in La Cira–Infantas

A current problem in oil recovery is knowledge of the reservoirs external geometry and the effect of geologic heterogeneities on hydrocarbon flow. Geologic heterogeneities compartmentalize reservoirs into flow units, preventing efficient drainage and sweep of reservoirs. Understanding 3-D geometry, spatial organization, genesis, and evolution of folds and faults is essential to providing a realistic conceptual model for flow studies. Wireline log-based structural models of mature giant oil fields, such as La Cira–Infantas, Colombia, do not provide clear delineation of production boundaries and compartments. In La Cira–Infantas, the characteristics and evolution of its structural style are not well understood. Published interpretations suggest that La Cira–Infantas is a complex combination of coeval folds, thrust faults, and frequent normal faults. The field is the westernmost expression of a thrust belt that extends out into the Middle Magdalena Valley Basin from the Eastern Cordillera Mountains. Yet structural elements are not concordant with characteristics of a typical compressive tectonic regime, as previously claimed in the literature. To solve these uncertainties, this paper presents a rigorous model of faulting for La Cira–Infantas. A very thorough and detailed description and documentation of the structural features, based on the interpretation of a 3-D seismic data set, support the model. La Cira–Infantas Field is in the Middle Magdalena Valley Basin (MMVB), near the center of Colombia, about 250 km north of Bogota (Figure 1). This basin is an elongated depression that covers approximately 30 000 km2. It is 500 km in length (N-S) and 60 km in width. Figure 1. Geologic map and producing oil fields of the Middle Magdalena Valley Basin. After Geotec (1988), Ingeominas (1976), and Ecopetrol (2000). MMVBs geologic evolution involved distinct stages of tectonic development: (1) aulocogen basin in the Triassic to Jurassic, (2) passive-margin basin in the Cretaceous to Paleocene, and (3) foreland basin …

Rock‐physics characterization of a Tertiary fluvial reservoir, La Cira‐Infantas oil field (Colombia)

Rock-physics analysis and modeling of a Tertiary fluvial reservoir in La Cira-Infantas oil field shows that velocity and acoustic impedance are reliable reservoir quality discriminators. High velocity and acoustic impedance correspond to shales while low velocity and acoustic impedance indicate high-quality sandstone reservoirs. Rock-physics-and-stratigraphy-based interpretation of Pwave seismic data appears to be a promising approach for the detection of depleted, bypassed and untapped reservoirs and deeper pools in mature fields such as La Cira-Infantas.

Textural sorting effect on elastic velocities, part I: laboratory observations, rock physics models, and applications to field data.

Summary Laboratory measurements, theoretical models, and field data indicate that in sediments and sedimentary rocks the deterioration of grain sorting results in a more efficient packing, thus causing the framework mixture to stiffen and the velocity to increase. Sediments and rocks with a grainor mud-supported fabric, and a low stage of textural maturity will show a high elastic velocity, in comparison with mature or supermature sediments. In contrast, if the rock framework is mechanically stable, well-sorted clastic rocks with a simple diagenesis will exhibit better reservoir properties and lower elastic velocities that poorly-sorted clastic rocks.

Introduction to special section: Seismic facies classification and modeling

Facies classification is a challenging task in formation evaluation analysis and seismic reservoir characterization. The facies classification model is a key element in the reservoir modeling work flow because the distribution of rock and elastic properties as well as the petroelastic models between

Rock physics and seismically derived impedance

Seismic impedance inversion is a commonly used procedure in practical geophysics. Many commercial packages are aimed at translating seismic traces that react to the contrast of the elastic properties into absolute impedance volumes. The goal of this operation is obvious: because the absolute rather than relative values of the elastic properties can be related to porosity, mineralogy, texture, and fluid via rock physics transforms, such transforms can, in principle, be applied to the seismically derived impedance volume to ascertain the rock properties in the subsurface. For a detailed review of methods and practices, we refer the reader to an encompassing treatise on impedance inversion by Latimer (2011) where a multitude of authors and their publications are listed. Here we present a case study by Dvorkin and Alkhater (2004) based on simple rock-physics-based logic to delineate porosity and fluid in an impedance section. The reservoir under examination consists of relatively soft sands. As a result, the acoustic impedance of the gas-saturated sand is much smaller than that of the oil- and water-saturated sand. This large impedance difference allows us to identify the pore fluid only from the P -wave data, without using offset information. As a result, we map both pore fluid and porosity by using impedance inversion applied to stacked seismic data.