ELF-EM Signal Processing While Drilling Based on Human-Computer Interaction Combined Algorithm

In the electromagnetic wave measurement while drilling(EM MWD), the extra low frequency electromagnetic wave(ELF-EM) below 20Hz was usually chosen as the carrier because of its transmission characteristics in the formation. However, as the drilling depth increases, the electromagnetic wave signals received on the ground gradually weaken, becoming lower than a certain signal-to-noise ratio(SNR)and making it impossible to be decoded or transmitted.The attenuation of electromagnetic wave in the formation is definitely one of the causes, but what matters more is the influence of environment noise at the well site, especially the in-band interference noise and random noise. Targeting at the out-of-band noise, the bandpass filter, which is invalid to the in-band noise,can be used to eliminate the noise out of the carrier’s main band. To cope with the question, an algorithm based on the human-computer interaction detection(HCID) was proposed in this paper that improves the SNR of ELF-EM signals, with the effective transmission distance of EM MWD increased. In this paper,the validity of the proposed HCID algorithm was verified through communication processing performance simulation and field data comparison, thus providing a reference for engineers and technicians in this field.Theoretical analysis and experimental data verification show that the combined algorithm decodes effectively under the in-band interference noise of-80d B SNR and in-band random noise of-17d B SNR.

An intelligent identification method of safety risk while drilling in gas drilling

In view of the shortcomings of current intelligent drilling technology in drilling condition representation, sample collection, data processing and feature extraction, an intelligent identification method of safety risk while drilling was established. The correlation analysis method was used to determine correlation parameters indicating gas drilling safety risk. By collecting monitoring data in the safety risk period of more than 20 wells, a sample database of a variety of safety risks in gas drilling was established, and the number of samples was expanded by using the method of few-shot learning. According to the forms of gas drilling monitoring data samples, a two-layer convolution neural network architecture was designed, and multiple convolution cores of different sizes and weights were set to realize the vertical and horizontal convolution computations of samples to extract and learn the variation law and correlation characteristics of multiple monitoring parameters. Finally, based on the training results of neural network, samples of different kinds of safety risks were selected to enhance the recognition accuracy. Compared with the traditional BP(error back propagation) full-connected neural network architecture, this method can more deeply and effectively identify safety risk characteristics in gas drilling, and thus identify and predict risks in advance, which is conducive to avoid and quickly solve safety risks while drilling. Field application has proved that this method has an identification accuracy of various safety risks while drilling in the process of gas drilling of about 90% and is practical.

A Petrophysical Approach to Evaluation from Measured While Drilling Gamma Ray, Case Study in the Powder River and Delaware Basins

One of the most common subsurface data sets that is easily accessible and often underutilized is the acquired measuring while drilling (MWD) gamma ray (GR-GAPI) log. Data is acquired from a given gamma ray tool positioned within the drill string and pulsed up to the surface through the mud column in the wellbore. Typical use of the data is for subsurface geologists, drillers and others to correlate the data to known stratigraphic signatures and steer wells through horizontal target zones. Through that correlation, an association to the geologic stratigraphic column can be made and the team of subsurface scientists adjusts where, how fast, and why they choose to continue drilling. The technique of correlation applies to both the conventional and unconventional application. In the unconventional application, the data is also typically acquired along the length of the horizontal wellbore. From a petrophysical standpoint, just acquiring a gamma ray can limit the amount of information and ability to fully evaluate the properties along the length of the well. In this study, we share and demonstrate how to utilize the MWD GR for petrophysical evaluation beyond just a volume of shale or volume of clay interpretation. The workflow will allow full integration of a comprehensive petrophysical evaluation that can then be utilized to support all subsurface understandings and modelling efforts.

Integrating geometallurgical ball mill throughput predictions into short-term stochastic production scheduling in mining complexes

This article presents a novel approach to integrate a throughput prediction model for the ball mill into short-term stochastic production scheduling in mining complexes.The datasets for the throughput prediction model include penetration rates from blast hole drilling(measurement while drilling),geological domains,material types,rock density,and throughput rates of the operating mill,offering an accessible and cost-effective method compared to other geometallurgical programs.First,the comminution behavior of the orebody was geostatistically simulated by building additive hardness proportions from penetration rates.A regression model was constructed to predict throughput rates as a function of blended rock properties,which are informed by a material tracking approach in the mining complex.Finally,the throughput prediction model was integrated into a stochastic optimization model for short-term production scheduling.This way,common shortfalls of existing geometallurgical throughput prediction models,that typically ignore the non-additive nature of hardness and are not designed to interact with mine production scheduling,are overcome.A case study at the Tropicana Mining Complex shows that throughput can be predicted with an error less than 30 t/h and a correlation coefficient of up to 0.8.By integrating the prediction model and new stochastic components into optimization,the production schedule achieves weekly planned production reliably because scheduled materials match with the predicted performance of the mill.Comparisons to optimization using conventional mill tonnage constraints reveal that expected production shortfalls of up to 7%per period can be mitigated this way.

Numerical modeling of DPSK pressure signals and their transmission characteristics in mud channels

A numerical model and transmission characteristic analysis of DPSK （differential phase shift keying） pressure signals in mud channels is introduced. With the control logic analysis of the rotary valve mud telemetry, a logical control signal is built from a Gate function sequence according to the binary symbols of transmitted data and a phase-shift function is obtained by integrating the logical control signal. A mathematical model of the DPSK pressure signal is built based on principles of communications by modulating carrier phase with the phase-shift function and a numerical simulation of the pressure wave is implemented with the mathematical model by MATLAB programming. Considering drillpipe pressure and drilling fluid temperature profile along drillpipes, the drillpipe of a vertical well is divided into a number of sections. With water-based drilling fluids, the impacts of travel distance, carrier frequency, drillpipe size, and drilling fluids on the signal transmission were studied by signal transmission characteristic analysis for all the sections. Numerical calculation results indicate that the influences of the viscosity of drilling fluids and volume fraction of gas in drilling fluids on the DPSK signal transmission are more notable than the others and the signal will distort in waveform with differential attenuations of the signal frequent component.

Trajectory Control: Directional MWD Inversely New Wellbore Positioning Accuracy Prediction Method

The deviation control of directional drilling is essentially the controlling of two angles of the wellbore actually drilled, namely, the inclination and azimuth. In directional drilling the bit trajectory never coincides exactly with the planned path, which is usually a plane curve with straight, building, holding, and dropping sections in succession. The drilling direction is of course dependant on the direction of the resultant forces acting on the bit and it is quite a tough job to hit the optimum target at the hole bottom as required. The traditional passive methods for correcting the drilling path have not met the demand to improve the techniques of deviation control. A method for combining wellbore surveys to obtain a composite, more accurate well position relies on accepting the position of the well from the most accurate survey instrument used in a given section of the wellbore. The error in each position measurement is the sum of many independent root sources of error effects. The relationship between surveys and other influential factors is considered, along with an analysis of different points of view. The collaborative work describes, establishes a common starting point of wellbore position uncertainty model, definition of what constitutes an error model, mathematics of position uncertainty calculation and an error model for basic directional service.