Abstract:
In one embodiment, a method includes obtaining a borehole image deriving from a downhole tool in a wellbore of a geological formation, identifying one or more patches that correspond to sediment particles on the fullbore image, computing one or more characteristics for each of the one or more patches. The one or more characteristics may include long/short axis length, size, roundness, sphericity, orientation, or some combination thereof. The method may also include displaying a visual representation for each of the one or more characteristics.
Abstract:
Apparatus and methods for obtaining first properties of a fluid, such as by estimating a second property of the fluid based on the first properties using a machine learning algorithm, propagating a first uncertainty of the first properties to a second uncertainty of the second property, generating an expected phase envelope of the fluid based on the second property, and generating a deviation phase envelope of the fluid based on the second uncertainty.
Abstract:
A method includes receiving first fluid property data from a first location in a hydrocarbon reservoir and receiving second fluid property data from a second location in the hydrocarbon reservoir. The method includes performing a plurality of realizations of models of the hydrocarbon reservoir according to a respective plurality of one or more plausible dynamic processes to generate one or more respective modeled fluid properties. The method includes selecting the one or more plausible dynamic processes based at least in part on a relationship between the first fluid property data, the second fluid property data, and the modeled fluid properties obtained from the realizations to identify potential disequilibrium in the hydrocarbon reservoir.
Abstract:
Various implementations directed to the integration of seismic data with downhole fluid analysis to predict the location of heavy hydrocarbon are provided. In one implementation, a method may include receiving seismic data for a hydrocarbon reservoir of interest. The method may also include identifying geological features associated with a secondary gas charge from the seismic data. The method may further include determining the proximity of the geological features to the hydrocarbon reservoir of interest. The method may additionally include receiving preliminary downhole fluid analysis (DFA) data from formations at or near the hydrocarbon reservoir of interest. The method may further include analyzing the preliminary DFA data to determine the equilibrium state of the hydrocarbon reservoir and to confirm the secondary gas charge in the hydrocarbon reservoir. The method may also include determining whether to perform one or more additional DFA's.
Abstract:
A method for performing contamination monitoring through estimation wherein measured data for optical density, gas to oil ratio, mass density and composition of fluid components are used to obtain plotting data and the plotting data is extrapolated to obtain contamination levels.
Abstract:
A method includes operating a downhole acquisition tool in a wellbore in a geological formation. The wellbore or the geological formation, or both, contains first fluid that includes a native reservoir fluid of the geological formation and a contaminant. The method also includes receiving a portion of the first fluid into the downhole acquisition tool and determining a plurality of properties of the portion of the first fluid using the downhole acquisition tool. The plurality of properties includes a mass fraction of a component of the portion of the first fluid and a density of the portion of the first fluid. The method also includes using the processor to estimate a volume fraction of the contaminant in the portion of the first fluid based at least in part on a composition mass fraction function that depends at least on the mass fraction of the component in the portion of the first fluid and the density of the portion of the first fluid.
Abstract:
A method, apparatus, and program product model address a modeling gap existing between basin and reservoir modeling through the use of a Reservoir Fluid Geodynamics (RFG) model usable for simulations conducted at a relatively fine spatial resolution and over a geological timescale.
Abstract:
A method includes identifying linearly behaving data within obtained data associated with fluid obtained from a subterranean formation. Shrinkage factor is determined based on the linearly behaving data. A function relating GOR data of the obtained fluid with the determined shrinkage factor is determined. A first linear relationship between optical density (OD) data of the obtained fluid and the function is determined. A second linear relationship between density data of the obtained fluid and the function is determined. An oil-based mud (OBM) filtrate contamination property of OBM filtrate within the obtained fluid based on the first linear relationship is determined. A native formation property of native formation fluid within the obtained fluid based on the second linear relationship is determined. A volume fraction of OBM filtrate contamination within the obtained fluid based on the OBM filtrate contamination property and the native formation property is estimated.
Abstract:
A method for performing contamination monitoring through estimation wherein measured data for optical density, gas to oil ratio, mass density and composition of fluid components are used to obtain plotting data and the plotting data is extrapolated to obtain contamination levels.
Abstract:
A downhole tool, surface equipment, and/or remote equipment are utilized to obtain data associated with a subterranean hydrocarbon reservoir, fluid contained therein, and/or fluid obtained therefrom. At least one condition indicating that a density inversion exists in the fluid contained in the reservoir is identified from the data. Molecular sizes of fluid components contained within the reservoir are estimated from the data. A model of the density inversion is generated based on the data and molecular sizes. The density inversion model is utilized to estimate the density inversion amount and depth and time elapsed since the density inversion began to form within the reservoir. A model of a gravity-induced current of the density inversion is generated based on the data and the density inversion amount, depth, and elapsed time.