AADE-18-FTCE-SPP-02 Learning from Experience : Using an Unsupervised Learning Real-Time Mud Contamination Monitoring Simulator in Umm Er Radhuma and Tayarat Hydrogen Sulfide Formations

Drilling boreholes through Umm Er Radhuma and Tayarat Formations southern Iraq has been a challenge due to Hydrogen Sulfide (H2S). In this era of increased concern for personal safety and environmental factors, the industry needs additional tools and methods for handling this deadly and corrosive gas. In this paper, formations field data are used to validate the simulator which monitors H2S and automatically helps the engineer adjust mud pH to neutralize free H2S. This paper describes a real-time monitoring mud contamination simulator which consists of a PC interface kit and MATLAB graphic user interface. The kit is provided with H2S indicator. The presented approach is based on the Umm Er Radhuma, and Tayarat Formations mud-logger field records in a way mimic the real-time processing sensors data. Based on real field data for five wells with historical hydrogen sulfide influx problem, two criteria are used to set the simulator alert levels: the Health Safety Environment (HSE) policy and the results of unsupervised learning for category clustering using Fuzzy Adaptive Resonance Theory (ART). This simulator invests different alert levels to enable proactive decision support. The system demonstrates a reliable response to H2S intrusion while drilling. It significantly improves a drilling operator’s ability to monitor the influx of H2S and to quickly and safely initiate appropriate treatment. Introduction Safely drilling boreholes through hydrogen sulfide bearing formations poses a critical challenge for the oil and gas industry [1, 2]. Furthermore, in addition to the acute rig-site safety issues with H2S, this gas also has a long-term detrimental effect as it is a major environmental pollutant from oil and gas production and processing [3]. Thus, a precise identification of the presence of H2S levels in a drilling fluid stream will improve the process quality in terms of personnel safety and lessened environmental impacts [4]. Therefore, innovative methodologies and automated tools are desired [5]. This integrated approach uses three systems: historical real-time drilling data with hydrogen sulfide influx to create an H2S problematic event, Fuzzy ART which is attractive because of its speed, scalability, incremental learning, and amenability to parallel implementation [6], and a real-time monitoring simulator mimicking H2S intrusion. The presented approach required precise interpretation for the H2S chemical-physical phenomena. Recalling the historical oilfield data (for the hydrogen sulfide formation) adopted from MATLAB helped to create a new retrievable H2S profile. To create an event triggering system, Fuzzy ART clustering allowed vector quantization of different alert levels for each variable. Furthermore, the H2S monitoring simulator is used to mimic the real field data, which is part of the project of the labscale real-time mud pH and electric conductivity monitoring system while drilling in hydrogen sulfide formations [7]. When an H2S influx occurs, the diagnosis of it is an effective strategy to avoid the harmful effects on health and the environment [8]. Hydrogen sulfide contamination is expensive, dangerously toxic to humans and animals, and extremely corrosive to most metals as it can cause cracking of drill pipe and tubular goods, and destruction of testing tools and wire lines [9]. Diagnosis is especially important in wells with a hydrogen sulfide bearing zone, in which the diagnosis is not obvious and problems will therefore take time to resolve. In this paper, the presented integrated approach can be evaluated and transformed into useful symptoms for an automatic diagnosis. AADE-18-FTCE-SPP-02 Learning from Experience: Using an Unsupervised Learning Real-Time Mud Contamination Monitoring Simulator in Umm Er Radhuma and Tayarat Hydrogen Sulfide Formations Ethar H. K. Alkamil1,2; Ahmed K. Abbas1,3; Ralph Flori1; Leonardo Enzo Brito da Silva4,5, and Donald C. Wunsch II4 1: Department of Geosciences and Geological and Petroleum Engineering, Missouri University of Science and Technology, Rolla, Missouri, USA. 2: Petroleum Engineering Department, University of Basrah, Iraq. 3: Iraqi Drilling Company, Basrah, Iraq 4: Applied Computational Intelligence Laboratory, Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO, USA. 5: CAPES Foundation, Ministry of Education of Brazil, Brasília, DF, Brazil 2 E. Alkamil, A. Abbas, R. Flori, L. E. Brito da Silva, and D. Wunsch II AADE-18-FTCE-SPP-02 Umm Er Radhuma and Tayarat Formations Overview The Umm Er Radhuma and Tayarat Formations are more homogenous formation in a limited aquifer of southern Iraq. They represent pale grey dolomitic marl, anhydrite and argillaceous dolomite with anhydrite nodules, argillaceous dolomite, dolomitic limestone, coarsely crystalline dolomite, and rare phosphatic grains. The thickness of both formations is around 500 m, ranging from the top 1000 m true vertical depth to the bottom of the formation at 1500 m [10, 11]. Sulphurous water may flow into the wellbore while drilling Umm Er Radhuma and Tayarat Formations. Methodology In this study, an integrated approach that consists of three systems is presented to create an H2S real-time monitoring system as shown in Figure 1. Figure 1. H2S Proactive System. Recall Historical Drilling Data As it is an ever-present factor during drilling it deserves and requires special concern. Especially today’s increased concern for personal safety and environmental factors, the industry needs additional tools and methods for handling this deadly and corrosive gas. Issues such as H2S intrusion create a significant source of human, environmental, and drilling related problems associated “red money” [12]. As a consequence, a key element in the safe and successful well construction process is the detailed monitoring of H2S [13]. The real-time monitoring concept is based on recognizing variations in expected behavior of rig sensor responses and keep tracking their rate of change (deviation from the accepted level) using hybrid algorithms [14]. This approach links analytic, statistic and knowledge based concepts, together with the rig H2S sensors on rig floor, shale shaker, and the H2S unit, mud electric conductivity sensor, and the pH manual readings, as shown in Figure 2. With a real data for wells with H2S intrusion at a high degree of operational detail, generated using Fuzzy ART automated operations recognition, as it will be discussed later in this work. It is possible to identify the number of clusters which represent different H2S alert levels. Figure 2. System overview (Adapted from [15]). Fuzzy Adaptive Resonance Theory Network Adaptive Resonance Theory (ART) is an unsupervised learning method that eliminates the “stability-plasticity dilemma". ART is capable of learning arbitrary data in both a stable and self-organizing manner [16]. ART1 [17] deals with binary data, whereas Fuzzy ART deals with arbitrary data. Henceforth, we will be referring to Fuzzy ART [16]. Before the training, the data pass through a preprocessing step during which they are scaled to fit into the range of [0,1] and complement-coded, as shown in Figure 3 [18]. The weight vectors wj are initialized to be all 1. Let x be an input sample. When choosing a category, the competition in layer F2 is calculated, defined as: Tj = |x Ʌ y| α + |wj| (1) where Ʌ is the fuzzy AND operator defined by: (x Ʌ y)i = min(xi, yi) (2) and α > 0 is the choice parameter. From the winner-take-all competition,