Simulation logging experiment and interpretation model of array production logging measurements in a horizontal well

The distributions of local velocity and local phase holdup along the radial direction of pipes are complicated because of gravity differentiation,and the distribution of fluid velocity fi eld changes along the gravity direction in horizontal wells.Therefore,measuring the mixture flow and water holdup is difficult,resulting in poor interpretation accuracy of the production logging output profile.In this paper,oil–water two-phase flow dynamic simulation logging experiments in horizontal oil–water two-phase fl ow simulation wells were conducted using the Multiple Array Production Suite,which comprises a capacitance array tool(CAT)and a spinner array tool(SAT),and then the response characteristics of SAT and CAT in diff erent fl ow rates and water cut production conditions were studied.According to the response characteristics of CAT in diff erent water holdup ranges,interpolation imaging along the wellbore section determines the water holdup distribution,and then,the oil–water two-phase velocity fi eld in the fl ow section was reconstructed on the basis of the fl ow section water holdup distribution and the logging value of SAT and combined with the rheological equation of viscous fl uid,and the calculation method of the oil–water partial phase fl ow rate in the fl ow section was proposed.This new approach was applied in the experiment data calculations,and the results are basically consistent with the experimental data.The total fl ow rate and water holdup from the calculation are in agreement with the set values in the experiment,suggesting that the method has high accuracy.

Framework of automated value stream mapping for lean production under the Industry 4.0 paradigm

For efficient use of value stream mapping(VSM)for multi-varieties and small batch production in a data-rich environment enabled by Industry 4.0 technologies,a systematic framework of VSM to rejuvenate traditional lean tools is proposed.It addresses the issue that traditional VSM requires intensive on-site investigation and replies on experience,which hinders decisionmaking efficiency in dynamic and complex environments.The proposed framework follows the data-information-knowledge hierarchy model,and demonstrates how data can be collected in a production workshop,processed into information,and then interpreted into knowledge.In this paper,the necessity and limitations of VSM in automated root cause analysis are first discussed,with a literature review on lean production tools,especially VSM and VSM-based decision making in Industry 4.0.An implementation case of a furniture manufacturer in China is presented,where decision tree algorithm was used for automated root cause analysis.The results indicate that automated VSM can make good use of production data to cater for multi-varieties and small batch production with timely on-site waste identification and analysis.The proposed framework is also suggested as a guideline to renew other lean tools for reliable and efficient decision-making.