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== Surface-Data Sensors == | == Surface-Data Sensors == | ||
<div class="mw-collapsible-content"> | <div class="mw-collapsible-content"> | ||
<br/>By analyzing cuttings, drilling mud, and drilling parameters for hydrocarbon-associated phenomena, we can develop a great deal of information and understanding concerning the physical properties of a well from the surface to final depth. A critical function in data analysis is familiarity with the different sensors used for gathering surface data. These sensors can be grouped as follows: | <br/>By analyzing cuttings, drilling mud, and drilling parameters for hydrocarbon-associated phenomena, we can develop a great deal of information and understanding concerning the physical properties of a well from the surface to final depth. A critical function in data analysis is familiarity with the different sensors used for gathering surface data. These sensors can be grouped as follows: | ||
*Depth Tracking. | *Depth Tracking. | ||
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</gallery><br/>All density measurements suffer if the drillstring is sliding in a high-angle or horizontal borehole with the gamma detectors pointing up (away from the bottom of the wellbore). To overcome this problem, orientation devices are often inserted in the toolstring. As the BHA is being made up, the offset between the density sleeve and the tool face is measured. Adjusting the location of the orientation device allows the density measurement to be set to the desired offset. While the drillstring is sliding to build angle, the density detectors can be oriented downward by setting the offset to 180°.<br/><br/>LWD porosity measurements use a source (typically americium beryllium) that emits neutrons into the formation. Neutrons arrive at the two detectors (near and far) in proportion to the amount they are moderated and captured by the media between the source and detectors. The best natural capture medium is hydrogen, generally found in the water, oil, and gas in the pore spaces of the formation. The ratio of neutron counts arriving at the detectors is calculated and stored in memory or transmitted to the surface. A high near/far ratio implies a high concentration of hydrogen in the formation and, hence, high porosity.<br/><br/>Neutron measurements are susceptible to a large number of environmental effects. Unlike wireline or LWD density measurements, the neutron measurement has minimal protection from mud effects. Neutron source/detector arrays are often built into a section of the tool that has a slightly larger OD than the rest of the string. The effect of centering the tool has been shown<ref name="r13">Allen, D.F., Best, D.L., Evans, M. et al. 1993. The Effect of Wellbore Condition on Wireline and MWD Neutron Density Logs. SPE Form Eval 8 (1): 50-56. 00020563. http://dx.doi.org/10.2118/20563-PA.</ref> to have a dramatic influence on corrections required compared to wireline ('''Fig. 15.18'''). Standoff between the tool and the formation requires corrections of approximately 5 to 7 porosity units (p.u.) per inch. Borehole-diameter corrections can range from 1 to 7 p.u./in. depending on tool design. Neutron porosity measurements are also affected by mud salinity, hydrogen index, formation salinity, temperature, and pressure. However, these effects are generally much smaller, requiring corrections of approximately 0.5 to 2.0 p.u.<br/><br/><gallery widths="300px" heights="200px"> | </gallery><br/>All density measurements suffer if the drillstring is sliding in a high-angle or horizontal borehole with the gamma detectors pointing up (away from the bottom of the wellbore). To overcome this problem, orientation devices are often inserted in the toolstring. As the BHA is being made up, the offset between the density sleeve and the tool face is measured. Adjusting the location of the orientation device allows the density measurement to be set to the desired offset. While the drillstring is sliding to build angle, the density detectors can be oriented downward by setting the offset to 180°.<br/><br/>LWD porosity measurements use a source (typically americium beryllium) that emits neutrons into the formation. Neutrons arrive at the two detectors (near and far) in proportion to the amount they are moderated and captured by the media between the source and detectors. The best natural capture medium is hydrogen, generally found in the water, oil, and gas in the pore spaces of the formation. The ratio of neutron counts arriving at the detectors is calculated and stored in memory or transmitted to the surface. A high near/far ratio implies a high concentration of hydrogen in the formation and, hence, high porosity.<br/><br/>Neutron measurements are susceptible to a large number of environmental effects. Unlike wireline or LWD density measurements, the neutron measurement has minimal protection from mud effects. Neutron source/detector arrays are often built into a section of the tool that has a slightly larger OD than the rest of the string. The effect of centering the tool has been shown<ref name="r13">Allen, D.F., Best, D.L., Evans, M. et al. 1993. The Effect of Wellbore Condition on Wireline and MWD Neutron Density Logs. SPE Form Eval 8 (1): 50-56. 00020563. http://dx.doi.org/10.2118/20563-PA.</ref> to have a dramatic influence on corrections required compared to wireline ('''Fig. 15.18'''). Standoff between the tool and the formation requires corrections of approximately 5 to 7 porosity units (p.u.) per inch. Borehole-diameter corrections can range from 1 to 7 p.u./in. depending on tool design. Neutron porosity measurements are also affected by mud salinity, hydrogen index, formation salinity, temperature, and pressure. However, these effects are generally much smaller, requiring corrections of approximately 0.5 to 2.0 p.u.<br/><br/><gallery widths="300px" heights="200px"> | ||
File:Devol2 1102final Page 666 Image 0001.png|'''Fig. 15.18—Effects of tool centering. (From Economides, Watters, and Dunn-Norman, Petroleum Well Construction, © 1998; reproduced by permission of John Wiley & Sons Ltd.)''' | File:Devol2 1102final Page 666 Image 0001.png|'''Fig. 15.18—Effects of tool centering. (From Economides, Watters, and Dunn-Norman, Petroleum Well Construction, © 1998; reproduced by permission of John Wiley & Sons Ltd.)''' | ||
</gallery><br/>Statistical effects on nuclear measurements are quite significant. Uncertainties increase as ROP increases. LWD nuclear measurements can be performed either while drilling or while tripping. LWD rates vary because of ROP changes, but they typically range from 15 to 200 ft/hr, whereas instantaneous logging rates can be significantly higher. Tripping rates can range from 1,500 to 3,000 ft/hr. Typical wireline rates are approximately 1,800 ft/hr and constant. Statistical uncertainty in LWD nuclear logging also varies with formation type. In general, log quality begins to suffer increased statistical uncertainties at logging rates above 100 ft/hr. This limits the value of logging while tripping to repeating formation intervals of particular interest.<br/><br/>'''''Acoustic Logging.''''' Ultrasonic caliper measurements while drilling were introduced principally for improving neutron and density measurements. Caliper transducers consist of two or more piezoelectric-crystal stacks placed in the wall of the drill collar. These transducers generate a high-frequency acoustic signal, which is reflected by a nearby surface (ideally, the borehole wall). The quality of the reflection is determined by the acoustic-impedance mismatch between the original and reflected signals. Often, there are difficulties in obtaining caliper measurement in wells with high drilling-fluid weights. Compared to the wireline mechanical caliper, the ultrasonic caliper provides readings with much higher resolution.<br/><br/>Acoustic-velocity data are important in many lithologies for correlation with seismic information. These data also can be a useful porosity indicator in certain areas. Shear-wave velocity also can be measured and used to calculate rock mechanical properties. Four main challenges in constructing an LWD acoustic tool are described as follows<ref name="r14">Aron, J., Masson, J.P., Plona, T.L. et al. 1994. Sonic Compressional Measurements While Drilling. Presented at the SPWLA 35th Annual Logging Symposium, Tulsa, Oklahoma, USA, 19-22 June. SPWLA-1994-SS.</ref>: | </gallery><br/>Statistical effects on nuclear measurements are quite significant. Uncertainties increase as ROP increases. LWD nuclear measurements can be performed either while drilling or while tripping. LWD rates vary because of ROP changes, but they typically range from 15 to 200 ft/hr, whereas instantaneous logging rates can be significantly higher. Tripping rates can range from 1,500 to 3,000 ft/hr. Typical wireline rates are approximately 1,800 ft/hr and constant. Statistical uncertainty in LWD nuclear logging also varies with formation type. In general, log quality begins to suffer increased statistical uncertainties at logging rates above 100 ft/hr. This limits the value of logging while tripping to repeating formation intervals of particular interest.<br/><br/>'''''Acoustic Logging.''''' Ultrasonic caliper measurements while drilling were introduced principally for improving neutron and density measurements. Caliper transducers consist of two or more piezoelectric-crystal stacks placed in the wall of the drill collar. These transducers generate a high-frequency acoustic signal, which is reflected by a nearby surface (ideally, the borehole wall). The quality of the reflection is determined by the acoustic-impedance mismatch between the original and reflected signals. Often, there are difficulties in obtaining caliper measurement in wells with high drilling-fluid weights. Compared to the wireline mechanical caliper, the ultrasonic caliper provides readings with much higher resolution.<br/><br/>Acoustic-velocity data are important in many lithologies for correlation with seismic information. These data also can be a useful porosity indicator in certain areas. Shear-wave velocity also can be measured and used to calculate rock mechanical properties. Four main challenges in constructing an LWD acoustic tool are described as follows<ref name="r14">Aron, J., Masson, J.P., Plona, T.L. et al. 1994. Sonic Compressional Measurements While Drilling. Presented at the SPWLA 35th Annual Logging Symposium, Tulsa, Oklahoma, USA, 19-22 June. SPWLA-1994-SS.</ref>: | ||
*Preventing the compressional wave from traveling down the drill collar and obscuring the formation arrival. Unlike wireline tools, the bodies of LWD tools must be rigid structural members that can withstand and transmit drilling forces down the BHA. Therefore, it is impractical to adopt the wireline solution of cutting intricate patterns into the body of the tool to delay the arrival of the compressional wave. Isolator design is crucial and is still implemented to enable successful signal processing in a wide variety of formations, particularly the slower ones<nowiki>[</nowiki> | *Preventing the compressional wave from traveling down the drill collar and obscuring the formation arrival. Unlike wireline tools, the bodies of LWD tools must be rigid structural members that can withstand and transmit drilling forces down the BHA. Therefore, it is impractical to adopt the wireline solution of cutting intricate patterns into the body of the tool to delay the arrival of the compressional wave. Isolator design is crucial and is still implemented to enable successful signal processing in a wide variety of formations, particularly the slower ones<nowiki>[</nowiki> | ||
those having a compressional delta time (Δt<sub>C</sub>) slower than approximately 100 μsec<nowiki>]</nowiki> | |||
those having a compressional delta time (Δt<sub>C</sub>) slower than approximately 100 μsec | |||
<nowiki>]</nowiki> | |||
. | . | ||
*Mounting transmitters and receivers on the OD of the drill collar without compromising their reliability. | *Mounting transmitters and receivers on the OD of the drill collar without compromising their reliability. | ||
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s depth. Driller<nowiki>’</nowiki> | s depth. Driller<nowiki>’</nowiki> | ||
s depths are based on measurements of the length of drillpipe going in the hole and are referenced to a device for measuring the height of the kelly or top drive with respect to a fixed point. These instantaneous measurements of depth are stored with respect to time for later merging with LWD downhole-memory data. The final log is constructed from this depth merge. On fixed installations, such as land rigs or jackup rigs, a number of well-documented sources exist that describe environmental error being introduced in the driller<nowiki>’</nowiki> | s depths are based on measurements of the length of drillpipe going in the hole and are referenced to a device for measuring the height of the kelly or top drive with respect to a fixed point. These instantaneous measurements of depth are stored with respect to time for later merging with LWD downhole-memory data. The final log is constructed from this depth merge. On fixed installations, such as land rigs or jackup rigs, a number of well-documented sources exist that describe environmental error being introduced in the driller<nowiki>’</nowiki> | ||
s depth method. Floating rigs can introduce additional errors. One study suggested that the following environmental errors would be introduced in a 3000-m well<ref name="r17">Kirkman, M. and Seim, P. 1989. Depth Measurement with Wireline and MWD Logs. In Measurement While Drilling, ed. Rollins et al., Vol. 40, 27-33. Richardson, Texas: Reprint Series, SPE.</ref>: | s depth method. Floating rigs can introduce additional errors. One study suggested that the following environmental errors would be introduced in a 3000-m well<ref name="r17">Kirkman, M. and Seim, P. 1989. Depth Measurement with Wireline and MWD Logs. In Measurement While Drilling, ed. Rollins et al., Vol. 40, 27-33. Richardson, Texas: Reprint Series, SPE.</ref>: | ||
*Drillpipe stretch: 5- to 6-m increase. | *Drillpipe stretch: 5- to 6-m increase. | ||
*Thermal expansion: 3- to 4-m increase. | *Thermal expansion: 3- to 4-m increase. | ||
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=== Value from Data === | === Value from Data === | ||
The shared use of information at the rigsite or data transmitted in real time or offline to the office is used for a variety of purposes that provide real value to the operator. Operators implement corporate stores of this information to realize several goals: | The shared use of information at the rigsite or data transmitted in real time or offline to the office is used for a variety of purposes that provide real value to the operator. Operators implement corporate stores of this information to realize several goals: | ||
*Enabling an open database to reliably store historical drilling, completion, and well-services information in a common data store. | *Enabling an open database to reliably store historical drilling, completion, and well-services information in a common data store. | ||
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=== Key Features and Functions of Project Data-Management Systems === | === Key Features and Functions of Project Data-Management Systems === | ||
'''''Broad Application Support.''''' Project data-management systems should support a rich set of E&P applications that solve a broad range of technical problems. Key workflows should be completed entirely within a system, without the use of external tools for data manipulation.<br/><br/>'''''Open Extensible Environment.''''' Given the diversity of the oil and gas industry, no software vendor can offer a solution to all problems. Instead, project-management systems should provide an open-development environment, allowing niche application vendors to plug in "best of breed" applications.<br/><br/>'''''Technology Based on a Standard Database.''''' Many requirements of an E&P project data-management system are similar to those of database systems in other industries. Systems based on common horizontal-market technologies allow the use of relatively cheap and powerful horizontal-market database tools.<br/><br/>The most mature database-management systems are "relational databases." Relational databases have been used by many industries to store mission-critical data for more than 25 years. Researchers at IBM performed much of the early research on the relational model in the late 1960s and early 1970s.<ref name="r18">Codd, E.F. 1983. A relational model of data for large shared data banks. Commun. ACM 26 (1): 64-69. http://dx.doi.org/10.1145/357980.358007.</ref> A relational model views data logically as a series of tables and columns, with a mathematical model for operations on these structures. The physical arrangement of data is hidden; instead, one depends only on a simple, logical view of tabular data. All data are reduced to the simple "flat" tabular form. Relational databases support SQL, which allows users to build queries that filter the rows of a single table. SQL queries can also combine data from one table with data from another on the basis of shared foreign key fields. This allows SQL statements to join data from multiple tables and build powerful ad hoc reports.<br/><br/>Relational database-management systems range from desktop databases to enterprise data-management systems. Robust database systems typically provide: | '''''Broad Application Support.''''' Project data-management systems should support a rich set of E&P applications that solve a broad range of technical problems. Key workflows should be completed entirely within a system, without the use of external tools for data manipulation.<br/><br/>'''''Open Extensible Environment.''''' Given the diversity of the oil and gas industry, no software vendor can offer a solution to all problems. Instead, project-management systems should provide an open-development environment, allowing niche application vendors to plug in "best of breed" applications.<br/><br/>'''''Technology Based on a Standard Database.''''' Many requirements of an E&P project data-management system are similar to those of database systems in other industries. Systems based on common horizontal-market technologies allow the use of relatively cheap and powerful horizontal-market database tools.<br/><br/>The most mature database-management systems are "relational databases." Relational databases have been used by many industries to store mission-critical data for more than 25 years. Researchers at IBM performed much of the early research on the relational model in the late 1960s and early 1970s.<ref name="r18">Codd, E.F. 1983. A relational model of data for large shared data banks. Commun. ACM 26 (1): 64-69. http://dx.doi.org/10.1145/357980.358007.</ref> A relational model views data logically as a series of tables and columns, with a mathematical model for operations on these structures. The physical arrangement of data is hidden; instead, one depends only on a simple, logical view of tabular data. All data are reduced to the simple "flat" tabular form. Relational databases support SQL, which allows users to build queries that filter the rows of a single table. SQL queries can also combine data from one table with data from another on the basis of shared foreign key fields. This allows SQL statements to join data from multiple tables and build powerful ad hoc reports.<br/><br/>Relational database-management systems range from desktop databases to enterprise data-management systems. Robust database systems typically provide: | ||
*Network access to data; flexible and powerful data security; tools for "hot backups," allowing a system to be backed up without shutting down; and recovery tools in case of a system crash. | *Network access to data; flexible and powerful data security; tools for "hot backups," allowing a system to be backed up without shutting down; and recovery tools in case of a system crash. | ||
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*Simple project database administration. | *Simple project database administration. | ||
<br/><br/>Project data-management tools should isolate end users from the complexity of working directly with the database when performing common data-management tasks.<br/><br/>Data import/export routines support a wide variety of common data-exchange formats. They should provide management for units of measure and unit conversion, if necessary. They also should convert surface locations between different map-projection systems. Other domain-specific functionality is provided when importing particular data types (e.g., the computation of wellbore paths from directional survey information).<br/><br/>Data browsing, querying, and editing tools should fill in the gaps left by horizontal-market query and browse tools, offering industry-specific displays for key data types such as well logs, production plots, and seismic displays. These tools should allow the updating and editing of project data and should enforce standard business rules and data integrity.<br/><br/>Project-administration tools should allow users with relatively little database knowledge to perform the following: | <br/><br/>Project data-management tools should isolate end users from the complexity of working directly with the database when performing common data-management tasks.<br/><br/>Data import/export routines support a wide variety of common data-exchange formats. They should provide management for units of measure and unit conversion, if necessary. They also should convert surface locations between different map-projection systems. Other domain-specific functionality is provided when importing particular data types (e.g., the computation of wellbore paths from directional survey information).<br/><br/>Data browsing, querying, and editing tools should fill in the gaps left by horizontal-market query and browse tools, offering industry-specific displays for key data types such as well logs, production plots, and seismic displays. These tools should allow the updating and editing of project data and should enforce standard business rules and data integrity.<br/><br/>Project-administration tools should allow users with relatively little database knowledge to perform the following: | ||
*Project database creation. | *Project database creation. | ||
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<nowiki>*</nowiki> | <nowiki>*</nowiki> | ||
Conversion factor is exact.</div></div>[[Category:PEH]] | Conversion factor is exact.</div></div>[[Category:PEH]] [[Category:Volume II - Drilling Engineering]] [[Category:1.12 Drilling measurement, data acquisition, and automation]] | ||
[[Category:1.12 Drilling measurement, data acquisition, and automation]] | |||
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