Abstract: A method of cross-tool optical fluid model validation includes selecting verified field data measured with a first sensor of an existing tool as validation fluids and selecting a second sensor for a new tool or on a different existing tool. The method may also include applying cross-tool optical data transformation to the validation fluids in a tool parameter space from the first sensor to the second sensor, and calculating the synthetic optical responses of the second sensor on the validation fluids through cross-space data transformation. The method may further include determining a new or adjusting an existing operational fluid model of the second sensor in a synthetic parameter space according to the candidate model performance evaluated on the validation fluids, and optimizing well testing and sampling operation based on real-time estimated formation fluid characteristics using the validated fluid models of the second sensor in an operating tool.

Abstract: Systems and methods for optical fluid identification approximation and calibration are described herein. One example method includes populating a database with a calculated pseudo optical sensor (CPOS) response of a first optical tool to a first sample fluid. The CPOS response of the first optical tool may be based on a transmittance spectrum of a sample fluid and may comprise a complex calculation using selected components of the first optical tool. A first model may be generated based, at least in part, on the database. The first model may receive as an input an optical sensor response and output a predicted fluid property. A second model may also be generated based, at least in part, on the database. The second model may receive as an input at least one known/measured fluid/environmental property value and may output a predicted pseudo optical sensor response of the first optical tool.

Abstract: Apparatus, methods, and systems related to a thin-layer spectroelectrochemistry cell; electrically coupling a second end of a working electrical wire lead, a second end of a counter electrical wire lead, and a second end of a reference electrical wire lead to a potentiostat; introducing a conductive fluid into a cell body in the spectroelectrochemistry cell; introducing a detection species into the cell body; introducing a sample into the cell body; applying a voltage potential across the transparent sample window to drive an electrochemical reaction between the detection species and the sample in the transparent sample window fluid; transmitting electromagnetic radiation into an optical path through the transparent sample window, thereby optically interacting the electromagnetic radiation with the transparent sample window fluid to generate modified electromagnetic radiation; receiving the modified electromagnetic radiation with a detector; and generating an output signal corresponding to a characteristic of

Abstract: Apparatus, systems, and methods may operate to select a subset of sensor responses as inputs to each of a plurality of pre-calibrated models in predicting each of a plurality of formation fluid properties. The sensor responses are obtained and pre-processed from a downhole measurement tool. Each of the plurality of predicted formation fluid properties are evaluated by applying constraints in hydrocarbon concentrations, geo-physics, and/or petro-physics. The selection of sensor responses and the associated models from a pre-constructed model base or a candidate pool are adjusted and reprocessed to validate model selection.

Abstract: A system for pressure testing a formation includes a downhole tool configured to measure formation pressure, storage containing pressure parameters of a plurality of simulated formation pressure tests, and a formation pressure test controller coupled to the downhole tool and the storage. For each of a plurality of sequential pressure testing stages of a formation pressure test, the formation pressure test controller 1) retrieves formation pressure measurements from the downhole tool; 2) identifies one of the plurality of simulated formation pressure tests comprising pressure parameters closest to corresponding formation pressure values derived from the formation pressure measurements; and 3) determines a flow rate to apply by the downhole tool in a next stage of the test based on the identified one of the plurality of simulated formation pressure tests.

Abstract: An example method includes performing validation testing on a tool using a plurality of reference fluids, the tool having a calibrated optical sensor installed therein that includes one or more optical elements. One or more tool sensor responses from the calibrated optical sensor may be obtained and pre-processed, and the one or more tool sensor responses may be compared with calibrated optical sensor responses derived from the calibrated optical sensor during calibration and thereby detecting one or more optical sensor anomalies. The one or more optical sensor anomalies may be evaluated through performance analysis with one or more candidate models, and an alternative candidate model may be selected to mitigate the one or more optical sensor anomalies. One or more remedial options may be pursued when the alternative candidate model fails to mitigate the one or more optical sensor anomalies.

Abstract: Dimensionality reduction systems and methods facilitate visualization, understanding, and interpretation of high-dimensionality data sets, so long as the essential information of the data set is preserved during the dimensionality reduction process. In some of the disclosed embodiments, dimensionality reduction is accomplished using clustering, evolutionary computation of low-dimensionality coordinates for cluster kernels, particle swarm optimization of kernel positions, and training of neural networks based on the kernel mapping. The fitness function chosen for the evolutionary computation and particle swarm optimization is designed to preserve kernel distances and any other information deemed useful to the current application of the disclosed techniques, such as linear correlation with a variable that is to be predicted from future measurements. Various error measures are suitable and can be used.

Abstract: The disclosed embodiments include a method, apparatus, and computer program product for determining a synthetic gas-oil-ratio for a gas dominant fluid. For example, one disclosed embodiment includes a system that includes at least one processor, and at least one memory coupled to the at least one processor and storing instructions that when executed by the at least one processor performs operations that include optimizing a gas-oil-ratio database using a genetic algorithm and a multivariate regression simulator and generating a synthetic gas-oil-ratio for a gas dominant fluid.

Abstract: Various embodiments include apparatus and methods to provide pipe analysis, annulus analysis, or one or more combinations of pipe analysis and annulus analysis with respect to one or more pipes in a wellbore. The analysis can include application of clustering and classification methods with respect to the status and the environment of the one or more pipes in the wellbore. In various embodiments, the clustering and classification can be used in characterizing borehole annular material including cement bond quality evaluation. Additional apparatus, systems, and methods are disclosed.

Abstract: Dimensionality reduction systems and methods facilitate visualization, understanding, and interpretation of high-dimensionality data sets, so long as the essential information of the data set is preserved during the dimensionality reduction process. In some of the disclosed embodiments, dimensionality reduction is accomplished using clustering, evolutionary computation of low-dimensionality coordinates for cluster kernels, particle swarm optimization of kernel positions, and training of neural networks based on the kernel mapping. The fitness function chosen for the evolutionary computation and particle swarm optimization is designed to preserve kernel distances and any other information deemed useful to the current application of the disclosed techniques, such as linear correlation with a variable that is to be predicted from future measurements. Various error measures are suitable and can be used.

Abstract: Systems and methods for simulating optical sensor response data for fluids in a wellbore are disclosed herein. A system comprises a downhole tool, an optical sensor coupled to the downhole tool, and a sensor information mapping module. The sensor information mapping module is operable to receive sensor response information associated with the optical sensor and a first fluid, receive sensor spectra information associated with the optical sensor, and receive fluid spectroscopy information associated with the first fluid. The sensor information mapping module is also operable to determine a transformation matrix using the sensor response information, the sensor spectra information, and the fluid spectroscopy information, and determine, using the transformation matrix, simulated sensor response information associated with the optical sensor and a second fluid.

Abstract: An example method includes performing validation testing on a tool using a plurality of reference fluids, the tool having a calibrated optical sensor installed therein that includes one or more optical elements. One or more tool sensor responses from the calibrated optical sensor may be obtained and pre-processed, and the one or more tool sensor responses may be compared with calibrated optical sensor responses derived from the calibrated optical sensor during calibration and thereby detecting one or more optical sensor anomalies. The one or more optical sensor anomalies may be evaluated through performance analysis with one or more candidate models, and an alternative candidate model may be selected to mitigate the one or more optical sensor anomalies. One or more remedial options may be pursued when the alternative candidate model fails to mitigate the one or more optical sensor anomalies.

Abstract: A method includes obtaining a plurality of master sensor responses with a master sensor in a set of training fluids and obtaining node sensor responses in the set of training fluids. A linear correlation between a compensated master data set and a node data set is then found for a set of training fluids and generating node sensor responses in a tool parameter space from the compensated master data set on a set of application fluids. A reverse transformation is obtained based on the node sensor responses in a complete set of calibration fluids. The reverse transformation converts each node sensor response from a tool parameter space to the synthetic parameter space, and uses transformed data as inputs of various fluid predictive models to obtain fluid characteristics. The method includes modifying operation parameters of a drilling or a well testing and sampling system according to the fluid characteristics.

Abstract: The disclosed embodiments include a method, apparatus, and computer program product for determining a synthetic gas-oil-ratio for a gas dominant fluid. For example, one disclosed embodiment includes a system that includes at least one processor, and at least one memory coupled to the at least one processor and storing instructions that when executed by the at least one processor performs operations that include optimizing a gas-oil-ratio database using a genetic algorithm and a multivariate regression simulator and generating a synthetic gas-oil-ratio for a gas dominant fluid.

Abstract: Apparatus, systems, and methods may operate to select a subset of sensor responses as inputs to each of a plurality of pre-calibrated models in predicting each of a plurality of formation fluid properties. The sensor responses are obtained and pre-processed from a downhole measurement tool. Each of the plurality of predicted formation fluid properties are evaluated by applying constraints in hydrocarbon concentrations, geo-physics, and/or petro-physics. The selection of sensor responses and the associated models from a pre-constructed model base or a candidate pool are adjusted and reprocessed to validate model selection.

Abstract: System, methods, and apparatuses for determining properties of a production fluid downhole are presented. In one instance, a system includes a sample-filled sensing device for vibrating a first suspended tube containing a sample of production fluid and producing a first response signal. The system also includes a reference-fluid sensing device with a second suspended tube containing a viscosity -tunable fluid therein. The system vibrates the second suspended tube to create a second response signal. The viscosity of the viscosity -tunable fluid is varied until it is deemed to match that of the sample production fluid. Other systems and methods are presented.

Abstract: Techniques for modeling a wellbore fluid that includes a base fluid and one or more fluid additives includes identifying a target viscosity profile of the wellbore fluid; determining an initial set of values of the fluid additives that are based at least in part on the target viscosity profile; determining, with one or more non-linear predictive models, a computed viscosity profile of the wellbore fluid and a computed set of values of the fluid additives based, at least in part, on the initial set of values of the fluid additives; comparing the computed viscosity profile and at least one of the computed set of values with a specified criteria of the wellbore fluid; and preparing, based on the comparison, an output including the computed viscosity profile and at least one of the computed set of values of a resultant wellbore fluid.

Abstract: Systems and methods for optical fluid identification approximation and calibration are described herein. One example method includes populating a database with a calculated pseudo optical sensor (CPOS) response of a first optical tool to a first sample fluid. The CPOS response of the first optical tool may be based on a transmittance spectrum of a sample fluid and may comprise a complex calculation using selected components of the first optical tool. A first model may be generated based, at least in part, on the database. The first model may receive as an input an optical sensor response and output a predicted fluid property. A second model may also be generated based, at least in part, on the database. The second model may receive as an input at least one known/measured fluid/environmental property value and may output a predicted pseudo optical sensor response of the first optical tool.

Abstract: Various embodiments include apparatus and methods to perform a correlation of data and/or a statistical analysis of data. A correlation of data can include generating a scalar product of measured responses with known responses and using a threshold to determine a correlation of the data. Statistical analysis of data can include generating a statistical measure of measurement data, comparing the statistical measure to a threshold value, and determining phase status of a fluid. Additional apparatus, systems, and methods are disclosed.

Abstract: The disclosed embodiments include a method, apparatus, and computer program product for generating a cross-sensor standardization model. For example, one disclosed embodiment includes a system that includes at least one processor; at least one memory coupled to the at least one processor and storing instructions that when executed by the at least one processor performs operations comprising selecting a representative sensor from a group of sensors comprising at least one of same primary optical elements and similar synthetic optical responses and calibrating a cross-sensor standardization model based on a matched data pair for each sensor in the group of sensors and for the representative sensor. In one embodiment, the at least one memory coupled to the at least one processor and storing instructions that when executed by the at least one processor performs operations further comprises generating the matched data pair, wherein the matched data pair comprises calibration input data and calibration output data.