Lessons Learnt Reprocessing a Noisy Onshore 3D Seismic Survey

An 1,100 km2 400-fold seismic survey was acquired over some of the largest sand dunes in UAE in 2007. Large sand dunes generate many challenges for seismic processing due to the irregular data acquisition, large statics caused by the significant difference between the sand and sabkha velocities, and a massive amount of reverberation noise that hides the signal in the data. Occidental and ADNOC Sour Gas reprocessed this survey from January 2019 to August 2020 to overcome the challenges of the strong sand dune noise. For the first time, it was processed through prestack depth migration (PSDM). The primary objectives of the reprocessing project were to get an accurate PSDM volume that tied all of the available well control data; and to derive as accurate seismic amplitudes as possible over the target reservoir interval from near to far offsets to enable elastic inversion for reservoir porosity and net-thickness prediction. Whilst the reprocessing project achieved the project objectives and generated good subsurface images, it did not run as smoothly as hoped, despite being processed by one of the premier multinational processing companies. The extremely large sand dunes, which are present across most of the survey area, created major imaging problems. Key technical lessons learnt during reprocessing included: (1) CRS errors occurred sporadically during acquisition, requiring correction; (2) the sand curve (Liner, 2008) worked well for sand dune static corrections for this data set; (3) near surface statics changed whilst the survey was acquired by up to 6 ms - each shot station needed to be corrected for these statics changes because the shot stations were acquired twice with a symmetric split recording spread; and (4) the contractor's standard post-migration processing sequence (gather flattening, radon, noise attenuation, stack) did not work well for this very noisy data set. Next time we work with similar data and require a high quality result, we know to double the estimated project timeline as every step in the processing sequence takes much longer than expected when the signal-to-noise ratio of the data is very low. The novelty of this work was that we obtained large improvements in the seismic stack by applying offline gather conditioning before calculating trim statics to optimally flatten the very noisy migrated offset vector tile (OVT) gathers, prior to running the final noise attenuation and stacking workflows. Without this offline gather conditioning, the trim statics workflow mostly aligned the noise and damaged the stack.

Feature-based sequencing optimization method for minimizing non-cutting energy consumption for CNC machine tools

There is variety scheme when a part with multiple features is processed in CNC machines, and hence, different feature sequencing during processing affects not only productivity but also energy consumption. This paper concentrates on the energy-saving strategy by optimizing the feature processing sequence in the part processing stage through reducing the energy consumption of the non-cutting process. The detailed energy model is established considering rapid feed and general feed path in the X, Y, Z+, Z- directions for analyzing the impact of processing feature sorting on reducing the energy consumption of parts processing. The feature sequencing optimization is carried out under the condition of fixed cutting parameters for specific machining features to better reveal the sequence influence on energy consumption and non-cutting time. Meanwhile, the energy consumption of the non-cutting of parts specifically includes the empty pass and an automatic tool change model, while the normal feed and the rapid feed are established in different moving axis, respectively. Based on the developed model, the genetic algorithm is used to solve the optimal processing sequence and the lowest processing energy consumption. Finally, a cutting orthogonal experiment is executed to collect energy consumption data, analyze the data and fit the data to establish a specific energy consumption model for each processing stage. A case study of a part with nine features is used to optimize sequencing, which shows the effectiveness and validity of the proposed method.

Coordinated optimization of production scheduling and maintenance activities with machine reliability deterioration

<p style='text-indent:20px;'>In this paper, we investigate a coordinated optimization problem of production and maintenance where the machine reliability decreases with the use of the machine. Lower reliability means the machine is more likely to fail during the production stage. In the event of a machine failure, corrective maintenance (CM) of the machine is required, and the CM of the machine will cause a certain cost. Preventive maintenance (PM) can improve machine reliability and reduce machine failures during the production stage, but it will also cause a certain cost. To minimize the total maintenance cost, we must determine an appropriate PM plan to balance these two types of maintenance. In addition, the tardiness cost of jobs is also considered, which is affected not only by the processing sequence of jobs but also by the PM decision. The objective is to find the optimal job processing sequence and the optimal PM plan to minimize the total expected cost. To solve the proposed problem, an improved grey wolf optimizer (IGWO) algorithm is proposed. Experimental results show that the IGWO algorithm outperforms GA, VNS, TS, and standard GWO in optimization and computational stability.</p>

Scheduling job shop problems with operators with respect to the maximum lateness

This paper deals with the problem of assigning operators to jobs, within a free assignment-changing mode, in a job-shop environment subject to a fixed processing sequence of the jobs. We seek an assignment of operators that minimizes the maximum lateness. Within this model, a job needs an operator during the entire duration of its processing. We show that the problem is 𝒩𝒫-hard when the number of operators is arbitrary and exhibit polynomial time algorithms for the cases involving one and two operators, respectively.

Results from the First Phase of the Seafloor Backscatter Processing Software Inter-Comparison Project

Seafloor backscatter mosaics are now routinely produced from multibeam echosounder data and used in a wide range of marine applications. However, large differences (>5 dB) can often be observed between the mosaics produced by different software packages processing the same dataset. Without transparency of the processing pipeline and the lack of consistency between software packages raises concerns about the validity of the final results. To recognize the source(s) of inconsistency between software, it is necessary to understand at which stage(s) of the data processing chain the differences become substantial. To this end, willing commercial and academic software developers were invited to generate intermediate processed backscatter results from a common dataset, for cross-comparison. The first phase of the study requested intermediate processed results consisting of two stages of the processing sequence: the one-value-per-beam level obtained after reading the raw data and the level obtained after radiometric corrections but before compensation of the angular dependence. Both of these intermediate results showed large differences between software solutions. This study explores the possible reasons for these differences and highlights the need for collaborative efforts between software developers and their users to improve the consistency and transparency of the backscatter data processing sequence.