There are many challenges that one might encounter in Production Data Analysis. Here is a sample of some of the common challenges which occur during analysis including pressure, flow rate, well completion and General. These issues must be recognized if not addressed in the case that a product examination sequence is conducted. Influence of data examination could vary from high, moderate to very high. The article under forth describes of the factors generated and importance of data examination.
This type of records analysis is one that researchers can depend on to give them the best results when dealing with mature fields. In creation information study, practical methods have been the ones that have become dominant and they are categorized into two being Decline Curve study DCA which does not entail reservoir characteristics and Type Curve Matching which is subjective.
Production is an effective study procedure that entails reasonable reservoir characteristics but it also has its shortcoming among which are for reservoir characterization wellhead and bottom hole information is required and it is not available in mature areas and second PDA toolkit does not accommodate a technique that integrates results from hundreds of individual wells.
If the tubing and casing pressure separate it could be due to liquid load-up or a leak in the tubes. Scatter in creation rates and flowing pressures could mean unstable flow. Flowing pressure with large-duration step changes indicate infrequent measurements thus comparison with flowrate information is crucial to ensure that the pressure information are competent. Deconvolution issue works around poor quality of information.
There is the fuzzy pattern recognition which when its study of every well is completed results get merged to determine the specific pattern that is in that particular area. This is important as it helps to understand all the aspects of an area like the sweet spots, wells not performing well and have information about all the wells. It begins by plotting the rate of creation.
This technique also has challenges facing it like it could be hard at times to reach to accurate estimate rates and pressure figures which are a necessity in this case. This becomes a challenge when flow rates fluctuate or depreciate suddenly causing pressure records to also fluctuate or depreciate and at the end result one is left dealing with figures that is inconsistent and not related.
Diagnostic plot implies that a certain feature will emerge from a given figures profile. The goal is to provide a broad spectrum of diagnostic insight of the plots. If significant information mismatch were to exit, then we would conclude that these creation figures are not correlated or possibly corrupted. Comparison of measured pressures and those pressures computed from deconvolution show only fair agreement.
When records has been collected and analyzed accurately, then it should be able to set grounds of accurately measured creation rate and the pressure records should produce vigorous and competent interpretation results that are comparable to estimates from analyzed transient records also an overall HM model must be presented to help compare the raw rate and pressure data.
This type of records analysis is one that researchers can depend on to give them the best results when dealing with mature fields. In creation information study, practical methods have been the ones that have become dominant and they are categorized into two being Decline Curve study DCA which does not entail reservoir characteristics and Type Curve Matching which is subjective.
Production is an effective study procedure that entails reasonable reservoir characteristics but it also has its shortcoming among which are for reservoir characterization wellhead and bottom hole information is required and it is not available in mature areas and second PDA toolkit does not accommodate a technique that integrates results from hundreds of individual wells.
If the tubing and casing pressure separate it could be due to liquid load-up or a leak in the tubes. Scatter in creation rates and flowing pressures could mean unstable flow. Flowing pressure with large-duration step changes indicate infrequent measurements thus comparison with flowrate information is crucial to ensure that the pressure information are competent. Deconvolution issue works around poor quality of information.
There is the fuzzy pattern recognition which when its study of every well is completed results get merged to determine the specific pattern that is in that particular area. This is important as it helps to understand all the aspects of an area like the sweet spots, wells not performing well and have information about all the wells. It begins by plotting the rate of creation.
This technique also has challenges facing it like it could be hard at times to reach to accurate estimate rates and pressure figures which are a necessity in this case. This becomes a challenge when flow rates fluctuate or depreciate suddenly causing pressure records to also fluctuate or depreciate and at the end result one is left dealing with figures that is inconsistent and not related.
Diagnostic plot implies that a certain feature will emerge from a given figures profile. The goal is to provide a broad spectrum of diagnostic insight of the plots. If significant information mismatch were to exit, then we would conclude that these creation figures are not correlated or possibly corrupted. Comparison of measured pressures and those pressures computed from deconvolution show only fair agreement.
When records has been collected and analyzed accurately, then it should be able to set grounds of accurately measured creation rate and the pressure records should produce vigorous and competent interpretation results that are comparable to estimates from analyzed transient records also an overall HM model must be presented to help compare the raw rate and pressure data.
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