A novel ensemble method based on SBLMD-ANN-MOPSO approach for predicting the milling stability regimes

In recent decades, lots of work has been done to mitigate self excited vibration effects in milling operations. Still, a robust methodology is yet to be developed that can suggest stability bounds pertaining to higher metal removal rate (MRR). In the present work, experimentally acquired acoustic signals in milling operation have been computed using a modified Local Mean Decomposition (SBLMD) technique in order to cite tool chatter features. Further, three artificial neural network (ANN) training algorithms viz. Resilient Propagation (RP), Conjugate Gradient-Based (CGP) and Levenberg-Marquardt Algorithm (LM) and two activation functions viz. Hyperbolic Tangent Sigmoid (TANSIG) and Log Sigmoid (LOGSIG) has been used to train the acquired chatter vibration and metal removal rate data set. Over-fitting or under-fitting issues may arise from the random selection of a number of hidden neurons. The solution to these problems is also proposed in this paper. Among these training algorithms and activation functions, a suitable one has been selected and further invoked to develop prediction models of chatter severity and metal removal rate. Finally, Multi-Objective Particle Swarm Optimization (MOPSO) has been invoked to optimize developed prediction models for obtaining the most favourable range of input parameters pertaining to stable milling with higher productivity.

An Advanced Ultra-Deep Resistivity Mapping Sensor Reduced Reservoir Uncertainty and Eliminated the Need for a Pilot Hole for the First Time; A Case Study from Offshore Abu Dhabi

The high reservoir uncertainty, due to the lateral distribution of fluids, results in variable water saturation, which is very challenging in drilling horizontal wells. In order to reduce uncertainty, the plan was to drill a pilot hole to evaluate the target zones and plan horizontal sections based on the information gained. To investigate the possibility of avoiding pilot holes in the future, an advanced ultra-deep resistivity mapping sensor was deployed to map the mature reservoirs, to identify formation and fluid boundaries early before penetrating them, avoiding the need for pilot holes. Prewell inversion modeling was conducted to optimize the spacing and firing frequency selection and to facilitate an early real-time geostopping decision. The plan was to run the ultra-deep resistivity mapping sensor in conjunction with shallow propagation resistivity, density, and neutron porosity tools while drilling the 8 ½-in. landing section. The real-time ultra-deep resistivity mapping inversion was run using a depth of inversion up to 120 ft., to be able to detect the reservoir early and evaluate the predicted reservoir resistivity. This would allow optimization of any geostopping decision. The ultra-deep resistivity mapping sensor delivered accurate mapping of low resistivity zones up to 85 ft. TVD away from the wellbore in a challenging low resistivity environment. The real-time ultra-deep resistivity mapping inversion enabled the prediction of resistivity values in target zones prior to entering the reservoir; values which were later crosschecked against open-hole logs for validation. The results enabled identification of the optimal geostopping point in the 8 ½-in. section, enabling up to seven rig days to be saved in the future by eliminating a pilot hole. In addition this would eliminate the risk of setting a whipstock at high inclination with the subsequent impact on milling operations. In specific cases, this minimizes drilling risks in unknown/high reservoir pressure zones by improving early detection of formation tops. Plans were modified for a nearby future well and the pilot-hole phase was eliminated because of the confidence provided by these results. Deployment of the ultra-deep resistivity mapping sensor in these mature carbonate reservoirs may reduce the uncertainty associated with fluid migration. In addition, use of the tool can facilitate precise geosteering to maintain distance from fluid boundaries in thick reservoirs. Furthermore, due to the depths of investigation possible with these tools, it will help enable the mapping of nearby reservoirs for future development. Further multi-disciplinary studies remain desirable using existing standard log data to validate the effectiveness of this concept for different fields and reservoirs.

Optimising Depth Selection on Section Milling Operation to Secure Sustained Casing Pressure During Plug and Abandonment Operation

D-01 was an exploration well requiring a Plug-and-Abandonment (P&A) procedure with sustained casing pressure up to 2,000 psi on the B annulus. The presence of Sustained Casing Pressure (SCP) is one of the major technical challenges to decommission and abandon the well safely. Several attempts to secure the well using the perforation-and-squeeze method were performed – but failed. It was decided to perform section milling operations to create a viable rock-to-rock barrier. In this operation, the key factor in determining success, is selecting the correct depth to mill safely and secure the annular pressure source. A comprehensive approach was taken to determine the optimum depth for the section milling by evaluating existing open-hole and cased-hole data. Additionally, triple-detector Pulsed Neutron Log (PNL) was also performed prior to the section milling operation. The triple-detector PNL tool offered not only behind casing porosity (TPHI) and sigma (SIGM) measurement, but also a relatively new measurement in the oil and gas industry called Fast Neutron Cross Section (FNXS), which were expected to provide more accurate gas detection and gauge the condition near the borehole. By combining all the logs and reservoir data, the milling interval was selected and the section milling and subsequent cement plug operations were performed. Evaluation of existing open-hole and cased-hole logs provided geological and petrophysical insights which were useful in determining the hydrocarbon source charging the B-annulus. Further analysis on PNL data provided indication of possible gas pockets in the B-annulus. This information was used to distinguish between shallower formation sources or gas pockets that were not yet bled off. The operation on D-01 successfully resolved the B-annulus issue and the well was properly abandoned per regulatory requirements. Considering the complexity and high cost of section milling operations, a thorough review of data and pre-job logging increases the probability of selecting the optimum intervals needed to successfully complete P&A operations on SCP wellbores.

Micro-Milling Process of Metals: A Comparison between Femtosecond Laser and EDM Techniques

Nowadays, micro-machining techniques are commonly used in several industrial fields, such as automotive, aerospace and medical. Different technologies are available, and the choice must be made considering many factors, such as the type of machining, the number of lots and the required accuracy specifications in terms of geometrical tolerances and surface finish. Lasers and electric discharge machining (EDM) are widely used to produce micro-components and are similarly unconventional thermal technologies. In general, a laser is particularly appreciated by the industry for the excellent machining speeds and for the possibility to machine essentially any type of materials. EDM, on the other hand, has a poor material removal rate (MRR) but can produce microparts on only electrically conductive workpieces, reaching high geometrical accuracy and realizing steep walls. The most common micro-application for both the technologies is drilling but they can make also milling operations. In this work, a comparison of femto-laser and EDM technologies was made focusing on micro-milling. Two features were selected to make the comparison: micro-channels and micro-pillars. The depth was varied on two levels for both features. As workpiece material, aluminum, stainless steel and titanium alloy were tested. Data regarding the process performance and the geometrical characteristics of the features were analyzed. The results obtained with the two technologies were compared. This work improves the knowledge of the micro-manufacturing processes and can help in the characterization of their capabilities.

Reclamation of Abandoned Mine Soil: A Review on Properties and Remediation Techniques

The environment which is a part of ecosystem is being polluted due to urbanization, rapid industrialization increased demands for resources in our day to day lives have left no resources untouched. Various anthropogenic activities such as mining and milling operations, which include grinding, screening, concentrating ores and removal of tailings, disposal of mine and mill waste water release toxic metals into the natural environment affecting the lithosphere. Reclamation is the process of restoring the environmental soundness of these distressed mine lands. It consists of governing all kinds of physical, chemical and biological inconvenience of land area or soil such as fertility, pH, microbial activities and different soil nutrient cycles that make the destructed land soil fertile. The main aim of the reclamation is to bring back the fertility of soil by increasing its N, P, K values and Carbon contents. There are various remediation technologies available for removal of heavy metal from contaminated mine soil, in this paper we have discussed in-situ remediation, physical remediation, chemical remediation and biological remediation technologies which are implemented across the globe.

Machine Learning of Surface Layer Property Prediction for Milling Operations

Tool wear and cutting parameters have a significant effect on the surface layer properties in milling. Since the relation between tool wear, cutting parameters, and surface layer properties is mostly unknown, the latter cannot be controlled during production and may vary from part to part as tool wear progresses. To account for this uncertainty and to prevent premature failure, components often need to be oversized or surface layer properties need to be adjusted in subsequent manufacturing processes. Several approaches have been made to obtain models that predict the surface layer properties induced by manufacturing processes. However, those approaches need to be calibrated with a considerable number of experimental trials. As trials are time-consuming and surface layer measurements are laborious, no industrial applications have been realized. Complex models have one major drawback. They have to be re-parameterized as soon as process characteristics change. Therefore, manual experimental parameterization does not appear to be a feasible approach for industrial application. A highly automated approach for the machine learning of the relation between tool wear, cutting parameters and surface layer properties is presented in this paper. The amount of obtained measurement data allows a fundamental analysis of the approach, which paves the way for further developments.