Umiat field in northern Alaska is a shallow, light-oil accumulation with an estimated original oil in place of more than 1.5 billion bbl and 99 bcf associated gas. The field, discovered in 1946, was never considered viable because it is shallow, in permafrost, and far from any infrastructure. Modern drilling and production techniques now make Umiat a more attractive target if the behavior of a rock, ice, and light oil system at low pressure can be understood and simulated.
The Umiat reservoir consists of shoreface and deltaic sandstones of the Cretaceous Nanushuk Formation deformed by a thrust-related anticline. Depositional environment imparts a strong vertical and horizontal permeability anisotropy to the reservoir that may be further complicated by diagenesis and open natural fractures.
Experimental and theoretical studies indicate that there is a significant reduction in the relative permeability of oil in the presence of ice, with a maximum reduction when connate water is fresh and less reduction when water is saline. A representative Umiat oil sample was reconstituted by comparing the composition of a severely weathered Umiat fluid to a theoretical Umiat fluid composition derived using the Pedersen method. This sample was then used to determine fluid properties at reservoir conditions such as bubble point pressure, viscosity, and density.
These geologic and engineering data were integrated into a simulation model that indicate recoveries of 12%–15% can be achieved over a 50-yr production period using cold gas injection from five well pads with a wagon-wheel configuration of multilateral wells.
Added on 28 February, 2014
The influence of moisture, temperature, coal rank, and differential enthalpy on the methane (CH4) and carbon dioxide (CO2) sorption capacity of coals of different rank has been investigated by using high-pressure sorption isotherms at 303, 318, and 333 K (CH4) and 318, 333, and 348 K (CO2), respectively. The variation of sorption capacity was studied as a function of burial depth of coal seams using the corresponding Langmuir parameters in combination with a geothermal gradient of 0.03 K/m and a normal hydrostatic pressure gradient. Taking the gas content corresponding to 100% gas saturation at maximum burial depth as a reference value, the theoretical CH4 saturation after the uplift of the coal seam was computed as a function of depth. According to these calculations, the change in sorption capacity caused by changing pressure, temperature conditions during uplift will lead consistently to high saturation values. Therefore, the commonly observed undersaturation of coal seams is most likely related to dismigration (losses into adjacent formations and atmosphere). Finally, we attempt to identify sweet spots for CO2-enhanced coalbed methane (CO2-ECBM) production. The CO2-ECBM is expected to become less effective with increasing depth because the CO2-to-CH4 sorption capacity ratio decreases with increasing temperature and pressure. Furthermore, CO2-ECBM efficiency will decrease with increasing maturity because of the highest sorption capacity ratio and affinity difference between CO2 and CH4 for low mature coals.
Added on 31 January, 2014
Sandstone pressures follow the hydrostatic gradient in Miocene strata of the Mad Dog field, deep-water Gulf of Mexico, whereas pore pressures in the adjacent mudstones track a trend from well to well that can be approximated by the total vertical stress gradient. The sandstone pressures within these strata are everywhere less than the bounding mudstone pore pressures, and the difference between them is proportional to the total vertical stress. The mudstone pressure is predicted from its porosity with an exponential porosity-versus-vertical effective stress relationship, where porosity is interpreted from wireline velocity. Sonic velocities in mudstones bounding the regional sandstones fall within a narrow range throughout the field from which we interpret their vertical effective stresses can be approximated as constant. We show how to predict sandstone and mudstone pore pressure in any offset well at Mad Dog given knowledge of the local total vertical stress. At Mad Dog, the approach is complicated by the extraordinary lateral changes in total vertical stress that are caused by changing bathymetry and the presence or absence of salt. A similar approach can be used in other subsalt fields. We suggest that pore pressures within mudstones can be systematically different from those of the nearby sandstones, and that this difference can be predicted. Well programs must ensure that the borehole pressure is not too low, which results in borehole closure in the mudstone intervals, and not too high, which can result in lost circulation to the reservoir intervals.
Added on 31 January, 2014
This article describes a 250-m (820-ft)-thick upper Eocene deep-water clastic succession. This succession is divided into two reservoir zones: the lower sandstone zone (LSZ) and the upper sandstone zone, separated by a package of pelitic rocks with variable thickness on the order of tens of meters. The application of sequence-stratigraphic methodology allowed the subdivision of this stratigraphic section into third-order systems tracts.
The LSZ is characterized by blocky and fining-upward beds on well logs, and includes interbedded shale layers of as much as 10 m (33 ft) thick. This zone reaches a maximum thickness of 150 m (492 ft) and fills a trough at least 4 km (2 mi) wide, underlain by an erosional surface. The lower part of this zone consists of coarse- to medium-grained sandstones with good vertical pressure communication. We interpret this unit as vertically and laterally amalgamated channel-fill deposits of high-density turbidity flows accumulated during late forced regression. The sandstones in the upper part of this trough are dominantly medium to fine grained and display an overall fining-upward trend. We interpret them as laterally amalgamated channel-fill deposits of lower density turbidity flows, relative to the ones in the lower part of the LSZ, accumulated during lowstand to early transgression.
The pelitic rocks that separate the two sandstone zones display variable thickness, from 35 to more than 100 m (115–>328 ft), indistinct seismic facies, and no internal markers on well logs, and consist of muddy diamictites with contorted shale rip-up clasts. This section is interpreted as cohesive debris flows and/or mass-transported slumps accumulated during late transgression.
The upper sandstone zone displays a weakly defined blocky well-log signature, where the proportion of sand is higher than 80%, and a jagged well-log signature, where the sand proportion is lower than 60%. The high proportions of sand are associated with a channelized geometry that is well delineated on seismic amplitude maps. Several depositional elements are identified within this zone, including leveed channels, crevasse channels, and splays associated with turbidity flows. This package is interpreted as the product of increased terrigenous sediment supply during highstand normal regression.
Added on 31 January, 2014
In reservoir engineering, hydrodynamic properties can be estimated from downhole electrical data using heuristic models (e.g., Archie and Kozeny-Carman's equations) relating electrical conductivity to porosity and permeability. Although proven to be predictive for many sandstone reservoirs, the models mostly fail when applied to carbonate reservoirs that generally display extremely complex pore network structures.
In this article, we investigate the control of the three-dimensional (3-D) geometry and morphology of the pore network on the electrical and flow properties, comparing core-scale laboratory measurements and 3-D x-ray microtomography image analysis of samples from a Miocene reefal carbonate platform located in Mallorca (Spain).
The results show that micrometer- to centimeter-scale heterogeneities strongly influence the measured macroscopic physical parameters that are then used to evaluate the hydrodynamic properties of the rock, and therefore, existing models might not provide accurate descriptions because these heterogeneities occur at scales smaller than those of the integration volume of the borehole geophysical methods. However, associated with specific data processing, 3-D imagery techniques are a useful and probably unique mean to characterize the rock heterogeneity and, thus, the properties variability.
Added on 01 January, 2014
It is quite common for reservoir engineers to adjust the geological modelling without recoursing to the geologists by multiplying the porosity, the permeability, the anisotropy (kv/kh), the relative permeabilities, the well factors and many other parameters within their numerical world.
Added on 15 January, 2014
Petroleum exploration in deep water settings is resulting in the discovery of many giant fields in reservoirs that accumulated in large channel systems on the continental slope. The architecture of these reservoirs is exceedingly complex. In the face of multi-billion dollar costs, it is more important than ever before to accurately characterize these reservoirs.
Added on 15 January, 2014
Shale formations can confound even the savviest geoscientist when it comes to determining the inner workings of the rock. After expert evaluation, even the most attractive prospecting deal can be a tough sell. And there’s almost always a new piece to each of these puzzles that requires some sophisticated high-tech explaining.
Added on 01 April, 2014
"Breakthrough elegance": ExxonMobil geologists Jeff Ottmann and Kevin Bohacs shared their highly-coveted knowledge on sweet spots and producibility thresholds at a recent Geosciences Technology Workshop on Unconventional Reservoir Quality.
Added on 01 February, 2014
Search and Discovery Article
In recent years, artificial intelligence techniques, and neural networks in particular, have gained popularity in solving complex nonlinear
problems. Permeability, porosity and fluid saturation are three fundamental characteristics of reservoir systems that are typically distributed in a
spatially non-uniform and non-linear manner. In this context, porosity and permeability prediction from well log data is well-suited to neural
networks and other computer-based techniques. The present study aims to estimate formation porosity and permeability from digital well log
data using an artificial neural network (ANN) approach. A representative case study from the Alberta Deep Basin is presented. Five well log
responses (Gamma Ray Log (GR), Deep Resistivity (RD), Formation Density (DEN), Neutron Porosity (PHIN) and Density Porosity (PHID))
are used as inputs in the ANN to predict porosity and permeability. Core porosity and permeability are used as target data in the ANN to test
the prediction. The accuracy of the ANN approach is tested by regression plots of predicted values of porosity and permeability with core
porosity and permeability respectively. Excellent matching of core data and predicted values reflects the accuracy of the technique. ANN is a
fast and accurate method for the prediction of reservoir properties and could be applied in reservoir modeling and characterization.
Added on 25 February, 2014