Directional oil well drilling requires high precision of the wellbore positioning inside the productive area. However, due to specifics of engineering design, sensors that explicitly determine the type of the drilled rock are located farther than 15m from the drilling bit. As a result, the target area runaways can be detected only after this distance, which in turn, leads to a loss in well productivity and the risk of the need for an expensive re-boring operation.We present a novel approach for identifying rock type at the drilling bit based on machine learning classification methods and data mining on sensors readings. We compare various machine-learning algorithms, examine extra features coming from mathematical modeling of drilling mechanics, and show that the real-time rock type classification error can be reduced from 13.5% to 9%. The approach is applicable for precise directional drilling in relatively thin target intervals of complex shapes and generalizes appropriately to new wells that are different from the ones used for training the machine learning model.
During the directional drilling, a bit may sometimes go to a nonproductive rock layer due to the gap about 20m between the bit and high-fidelity rock type sensors. The only way to detect the lithotype changes in time is the usage of Measurements While Drilling (MWD) data. However, there are no general mathematical modeling approaches that both well reconstruct the rock type based on MWD data and correspond to specifics of the oil and gas industry. In this article, we present a data-driven procedure that utilizes MWD data for quick detection of changes in rock type. We propose the approach that combines traditional machine learning based on the solution of the rock type classification problem with change detection procedures rarely used before in Oil&Gas industry. The data come from a newly developed oilfield in the north of western Siberia. The results suggest that we can detect a significant part of changes in rock type reducing the change detection delay from 20 to 1.8 meters and the number of false positive alarms from 43 to 6 per well.
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Summary In this paper, we present a methodology for determining lithological difference at the bottom of the well during drilling operations. Our approach is based on the analysis of mechanical parameters of drilling. These parameters are receiving as real-time time-series data. The central part of the methodology is a model based on the machine learning approach. Our model and the whole methodology can be applied to real drilling cases. The set of parameters that are required for the methodology can be collected from the typical mud logging station. The main use case for the methodology is an optimization of the geosteering process. The most modern geosteering approaches are based on the LWD data. It is the main restriction of common approaches for the adjustment of the direction of drilling. The problem is that the LWD sensors are placed for a few decimals meters before the bit in a typical Bottom Hole Assembly (BHA) design. As a result, these a few tens of meters are drilling in a "blind window". The methodology is illustrated on the historical data of drilling of the Novoportovskoe oilfield. At the current stage, the results of the testing show that suggested methodology can correctly classify two out of three cases of changes of lithotypes while drilling.
Summary In this paper we present a new data-driven methodology for a drilling bit position and direction determination. The model is based on machine learning approach and trained on a data collected in a real-time or near real-time: mechanical parameters of drilling, tool-face data, MWD/LWD data, etc. The proposed methodology might be an interest for directional drilling service companies, operator companies that develop low-thickness productive strata. One of the main advantages of the proposed approach is economic efficiency which it provides due to absence of additional costs associated with payments for additional man hours for precise trajectory and direction monitoring. Methodology allows to predict trajectory at any time of drilling. The methodology is illustrated on the historical data of drilling of one oilfield. At the current stage, the results of the testing show good quality. Blind test on 154 independent sliding episodes shows that median absolute error (MedAE) of depth, inclination and azimuth are 0.26 m, 0.25° and 0.42°. These errors will decrease after adding more wells and steps, which are described in future plans.
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