Diagnosis of downhole incidents for geological drilling processes using multi-time scale feature extraction and probabilistic neural networks

Journal Pre-proof Diagnosis of downhole incidents for geological drilling processes using multi-time scale feature extraction and probabilistic neural networks Yupeng Li, Weihua Cao, Wenkai Hu, Min Wu

PII:

S0957-5820(19)31767-7

DOI:

https://doi.org/10.1016/j.psep.2020.02.014

Reference:

PSEP 2111

To appear in:

Process Safety and Environmental Protection

Received Date:

7 September 2019

Revised Date:

4 February 2020

Accepted Date:

10 February 2020

Please cite this article as: { doi: https://doi.org/ This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier.

Diagnosis of downhole incidents for geological drilling processes using multi-time scale feature extraction and probabilistic neural networks Yupeng Lia,b , Weihua Caoa,b,∗, Wenkai Hua,b , Min Wua,b a

School of Automation, China University of Geosciences, Wuhan 430074, China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China

ro

of

b

Abstract

-p

In deep geological drilling processes, the geological environment becomes more complex with the increasing of the drilling depth; consequently, the risks of down-

re

hole incidents get higher. If not discovered in time, these downhole incidents may develop to serious drilling accidents, causing significant financial and environmen-

lP

tal losses. In this paper, a new method is proposed to diagnose downhole incidents by extracting trend features in multi-time scales and establishing a probabilistic neural network based diagnosis model. There are two major contributions: First,

ur na

a feature extraction method is proposed to produce trend features from original process signals in different time scales; Second, an incident diagnosis method based on a broad probabilistic neural network is proposed to achieve better diagnosis performance in an expanded input space. Industrial case studies are presented to demonstrate the effectiveness and practicability of the proposed method. Results

Jo

show that the proposed method has superior performance in diagnosing downhole incidents for geological drilling processes. ∗

Corresponding author at: School of Autamation, China University of Geosciences, Wuhan 430074 China. Tel.: +8627; fax: +8627 Email addresses: [email protected] (Yupeng Li), [email protected] (Weihua Cao), [email protected] (Wenkai Hu), [email protected] (Min Wu)

Preprint submitted to XXX

February 12, 2020

Keywords: Fault diagnosis, system safety, geological drilling, downhole incident, probabilistic neural network 1. Introduction Deep geological drilling is a complex industrial project that drills towards deep formations for purposes, such as the deep geological prospecting and re-

of

source exploration. Compared to oil and gas drilling processes, the hole diameter in geological drilling is smaller, the drilling depth is much greater, and the drilling

ro

cost is relatively lower (Gan et al., 2019). The geological environment becomes more complex with the increasing of the drilling depth; consequently, the risks

-p

of downhole incidents get higher. If not discovered in time, these downhole incidents may develop to serious drilling accidents, causing significant financial and

re

environmental losses. As reported in (Godhavn, 2010), the non-production time caused by accidents account for 20%-25% of the total drilling operation period

lP

in European wells. However, measurements in deep wells are hard to obtain due to the high temperature, high pressure, and large disturbances in harsh environments; as a consequence, judging downhole conditions usually relies on process

ur na

knowledge and experience of skilled drilling workers. Yet such knowledge is not always available and reliable whereas failing to diagnose downhole incidents may lead to the situation getting worse. Therefore, in real drilling projects, effective techniques for risk assessment and fault diagnosis are in great demand.

Jo

In recent years, a variety of new methods have been proposed, including the drilling failure analysis (Albdiry and Almensory, 2016), drilling risk assessment (Zhang et al., 2018a; Abimbola et al., 2015), and blowout modeling (Sun et al., 2018b; Bhandari et al., 2015). Albdiry and Almensory (2016) presented a review on failure analysis methods of drillstring that managed risks and achieved high drilling performance. Sun et al. (2018b) provided a safety assessment method 2

for oil drilling based on the empirical study and analytic network process. In (Wu et al., 2019), the hydrogen sulfide leakage risk was predicted by dynamic Bayesian networks combined with cause-consequence analysis. In view of that most risk analysis methods were only applicable to one drilling operation stage, Abimbola and Khan (2016) proposed dynamic consequence models that considered the bottom-hole pressure for different drilling operation stages. Liu et al.

of

(2016) proposed a comprehensive risk assessment method for both the planning phase and the operational phase of drilling projects. He et al. (2018) proposed

ro

a Quantitative Risk Analysis (QRA) model for offshore kick failures by taking

into account uncertainties. For blowout modeling, Perez and Tan (2018) pro-

-p

posed an accident precursor probabilistic method as a supplement to the existing QRA by considering risk influencing factors and current operational conditions.

re

Sule et al. (2019) presented an advanced dynamic blowout risk modeling method, which described the dynamic risk with a dynamic Bayesian network and assessed

lP

the safety through dynamic risk analysis. To reduce the risk of the blowout, leading indicators for drilling operations were defined in (Tamim et al., 2017, 2019). These methods provide useful tools to evaluate failures and assess risks in drilling

ur na

operations, and thus may effectively prevent the occurrence of drilling accidents. Nevertheless, if an incident occurs, it is necessary to determine what the exact incident is and achieve early diagnosis in drilling operations. The state of the art of downhole incidents diagnosis involves two main categories, namely, the mechanism modeling based diagnosis and the data driven diagnosis. In the first

Jo

category, mathematical models are established based on process operation mechanisms. Methods, such as the state-parameter estimation and the redundancy analysis were proposed to diagnose drilling anomalies, such as the lost circulation, blowout, and drilling tool failures (Nikoofard et al., 2017; Willersrud et al., 2015; Zhou et al., 2011). In (Nikoofard et al., 2017), the joint unscented Kalman 3

filter and the extended Kalman filter were applied to estimate unmeasured states based on the bottom-hole pressure, outlet pressure, and outlet flow rate. A mechanism modeling method based on the adaptive observer and the statistical change monitoring was proposed in (Willersrud et al., 2015) to diagnose lost circulations. However, the mechanism based diagnosis requires that the mechanism knowledge is explicit, comprehensive, and up-to-date, whereas obtaining such knowledge is

of

usually difficult and expensive. By contrast, the data driven diagnosis is more

flexible and therefore has received increasing attentions due to the availability of

ro

sufficient drilling data and the development of big data techniques.

There emerge some novel data driven approaches for fault diagnosis in drilling

-p

systems, such as pattern recognition (Moazzeni et al., 2012; Sun et al., 2018a) and statistical process monitoring (Gao et al., 2018). A neural network based pattern

re

recognition method was developed to detect lost circulations from both the operational and geological data (Moazzeni et al., 2012). A slow feature analysis based

lP

downhole fault detection method was presented in (Gao et al., 2018) to distinguish normal set-point changes from faults. Sheremetov et al. (2008) proposed a fuzzy expert system to solve lost circulations based on the extraction of expert

ur na

rules from historical anomaly data. However, these methods only exploited original process signals and ignored features, such as variational trends. In this vein, qualitative trend features were extracted and exploited for fault diagnosis, and found to achieve good performance (Zhang et al., 2018b; Nayeem et al., 2016). For example, for incidents diagnosis in a shale-gas well fracturing process, quali-

Jo

tative trends were extracted and used as the input of a multi-class support vector machine to diagnose downhole incidents (Zhang et al., 2018b). Nayeem et al. (2016) extracted variational trends of drilling process signals and detected sudden changes for the monitoring of well kicks. These data driven approaches focus on solving one certain downhole incident, whereas diagnosing multiple downhole 4

incidents remains a difficult problem. Motivated by the above discussions, this paper aims at achieving diagnosis of multiple downhole incidents for geological drilling processes. In view of that drilling process signals show different paces with their mean changes in the presence of downhole incidents, a data driven incidents diagnosis method is proposed

a multi-time scale framework. The contributions are two folds:

of

to diagnose downhole incidents based on a broad probabilistic neural network in

original process signals in multi-time scales;

ro

1. A feature extraction method is proposed to produce trend features from

-p

2. An incident diagnosis method based on a broad probabilistic neural network is proposed to achieve better diagnosis performance with an expanded input

re

feature space.

Case studies with historical data from real drilling processes are presented to

lP

demonstrate the effectiveness of the proposed method. The remainder of this paper is organized as follows: Preliminaries about geological drilling and common downhole incidents are introduced in Section 2.

ur na

The systematic method for downhole incident diagnosis is presented in Section 3, including the extraction of trend features, incident diagnosis modeling, and implementation procedures. The effectiveness of the proposed method is demonstrated by case studies in Section 4, followed by conclusions in Section 5.

Jo

2. Geological Drilling Processes and Downhole Incidents The schematic of a deep geological drilling process is shown in Fig. 1. The

mud pump transmits the drilling fluid into the drill bit though the drillstring at a certain Strokes Per Minute (SPM). Then, the drilling fluid returns to the mud pit on the topside through the annular between the drillstring and the well 5

wall. The draw work above the drillstring changes the downhole Weight On Bit (WOB), and the rotary table drives the drillstring with a certain Rotation Per Minute (RPM). Rotating drillstring with a drill bit crushes the formation. Cuttings at the bottom of the well are carried by the drilling fluid to the topside. The drilling fluid balances the formation pressure and ensures the safety of the well. The drilling process is manipulated by three critical variables, namely the

of

WOB, RPM, and SPM.

SPP

TRQ

SPM

RPM

ro

Overpulling

Mud pit

-p

Mud

MPV

re

MFO

Drillstring

Drillstring washout

lP

Well wall

ur na

Lost circulation

Formation

Stuke pipe

Bottom hole assembly WOB

Jo

Figure 1: Schematic of a deep geological drilling process. The blue arrows represent the directions of drilling fluid flows. The red arrows point to downhole incidents highlighted by dashed red rectangles. Process variables are highlighted by dashed green rectangles.

The early diagnosis of downhole incidents is of great importance to a drilling

process, so as to prevent incidents from developing to serious drilling accidents, such as the blowout and wellbore collapse. In deep drilling processes, the following

6

downhole incidents are commonly seen: Lost circulation (denoted by c1 ) is known as the leakage of drilling fluid from the annulus into the formation. This incident usually happens when drilling into low pressure, crack, or soft formations (Al-Hameedi et al., 2018). In such situations, the drilling fluid pressure is greater than the formation pressure,

of

so that the liquid penetrates into the formation easily. Stuke pipe (denoted by c2 ) is referred to as a situation that the drill bit cannot

ro

drill into the rock or touch the bottom of the well, shown at the bottom of Fig. 1. It is usually resulted from the wellbore reduction or collapse due to

-p

the mismatch between the drilling performance and the formation lithology. If not discovered in time, it may develop to more serious accidents, such as

re

a drillstring failure or even a well collapse (Albdiry and Almensory, 2016). Drillstring washout (denoted by c3 ) is an incident that the drilling fluid leaks

lP

into the annulus from a drillstring hole, usually owing to the aging of drilling tools. If not solved timely, it may lead to worse situations, such as the

ur na

falling-off of drilling tools (Willersrud et al., 2013). Overpulling (denoted by c4 ) manifests as too much upward pull from the draw work at the top of Fig. 1. It usually occurs when drilling into the sand-shale, carbon mud, or pulverized coal. In some severe cases, the drillstring could

Jo

even be cut off (Wang, 2013).

In addition, a normal condition is denoted by c0 , indicating none of the above incidents occurs. Generally, the following key drilling variables are measured, including the Mud

Pit Volume (MPV, x1 ), StandPipe Pressure (SPP, x2 ), Strokes Per Minute (SPM, x3 ), Mud Flow Out (MFO, x4 ), Weight On Bit (WOB, x5 ), Rotation Per Minute 7

(RPM, x6 ), and Torque (TRQ, x7 ). They are also used in some existing studies on drilling systems (Skalle et al., 2013; Adedigba et al., 2018). The presence of a downhole incident may lead to significant variations in signals of these drilling variables, which usually show different paces with their mean changes. Such a characteristic makes downhole incidents distinguishable based on the variational trends of drilling process signals. Table 1 summarizes the information of these

Table 1: Summary of drilling process variables.

-p

ro

Full names Units Mud Pit Volume m3 StandPipe Pressure MPa Strokes Per Minute L/min Mud Flow Out m3 /min Weight On Bit kN Rotation Per Minute r/min Torque kN⋅m

re

Abbreviations MPV SPP SPM MFO WOB RPM TRQ

lP

Symbols x1 x2 x3 x4 x5 x6 x7

of

drilling variables, including symbols, abbreviations, full names, and units.

The objective of this work is to diagnose downhole incidents for deep geological drilling processes. In practice, it is usually that only one downhole incident

ur na

happens at most every time and it is rare to see the above mentioned incidents occur simultaneously. Therefore, this makes the downhole incident diagnosis naturally a multi-class learning problem: Given a labeled data set D = {(x, y) ∶ x ∈ RM , y ∈ C}, the objective is to train a classifier h such that h ∶ x → y, y ∈ C , where x = [x1 , x2 , ⋯, xM ]T represents a sample vector of drilling process variables,

Jo

y indicates the incident associated with x, and C = {ci ∶ i = 0, 1, ⋯, D} denotes the

set of downhole incidents. Then, the incident can be predicted for a new instance x∗ based on the well trained classifier h. In this work, there are seven critical drilling process variables (M = 7) involved and four incidents (D = 4) studied. In view of that the process signals show different variational trends, such as 8

slow varying trends and fast step trends, a systematic downhole incident diagnosis method in a multi-time scale framework is proposed to solve the above problem. Two major steps are involved: First, trend features, including step change features and slow varying features, are extracted from original process signals based on analysis in different time scales; Second, a downhole incident diagnosis model is established based on the Probabilistic Neural Networks (PNN) with mixed inputs,

ro

3. Downhole Incidents Diagnosis Method

of

including the original signals and extracted features.

This section proposes a systematic method to diagnose downhole incidents for

-p

deep drilling processes. A feature extraction step is presented to extract variational trends in multi-time scales. Then, downhole incidents are diagnosed through

re

multi-class learning by PNN with an expanded input space. The implementation

lP

procedures and performance metrics are given in the end of this section. 3.1. Features extraction in multi-time scales In this subsection, characteristics of variational trends in different time scales

ur na

are analyzed, and a feature extraction method is proposed to distill slow varying features and step change features in long and short time scales, respectively. 3.1.1. Characteristics of variational trends in different time scales According to the operation mechanisms of drilling processes and the expertise

Jo

from skilled engineers, the relation between each downhole incident and the variational trends of all the process signals are usually deterministic (Sun, 2006). Wang (2010) analyzed variational trends based on drilling operation manuals, logging data figures, and downhole incident data. It is concluded that some variables, including MPV, SPP, SPM, and MFO, show slow varying trends, whereas other

9

variables, including WOB, RPM, and TRQ, have much faster variational paces, usually reflecting step change trends. Therefore, the drilling process variables can be categorized into two groups, namely, slow varying ones and step change ones. Accordingly, the analysis to the two groups should be conducted separately. Thus, a multi-time scale based feature extraction is proposed to distill variational trends from original drilling process signals; this is an important step to extract useful

ur na

lP

re

-p

ro

of

features, so as to improve the performance of incident diagnosis.

Figure 2: Examples of process signals with the step change characteristic (WOB, x5 ) and the slow varying characteristic (MPV, x1 ).

Two examples of different variational trends are shown in Fig. 2, including a slow varying trend and a step change trend. For instance, WOB has a fast step

Jo

change trend in Fig. 2(a). In this scenario, a short time scale (e.g., as short as one sampling period) should be used to analyze RPM, WOB, and TRQ, so as to detect step change features as early as possible. By contrast, MPV changed much slower under a lost circulation incident. Fig. 2(b) displays the original signal of MPV, which shows a slow decreasing trend. Therefore, a long time scale should 10

be exploited to analyze MPV, SPP and MFO. 3.1.2. Extraction of step change features in a short-time scale In a short time scale, a meaningful trend feature is referred to as a step change. For variables showing step change trends, their trend features are taken from onestep differences in a sliding window. Meanwhile, a hypothesis test to the mean

of

change is utilized to examine whether a step change occurs in a signal. Detailed feature extraction method is presented as follows.

by

(1)

-p

∆x(t) = x(t) − x(t − τ ),

ro

Given a process signal x(t), the signal form of its one-step difference is given

where τ denotes the sampling period. To extract a step change feature, a sliding

of ∆x(t) are

t

∆x(k) , w k=t−w+1 ∑

lP

µ=

re

window is used. For each time window [t − w, t], the mean and standard deviation

¿ Á t À ∑ σ=Á

2

(∆x(k) − µ ) /(w − 1).

(2)

(3)

ur na

k=t−w+1

The hypothesis test to determine whether a process signal has a step change trend is given by

⎧ ⎪ ⎪ ⎪ H0 ∶ µ0 = µ1 , ⎨ ⎪ ⎪ ⎪ ⎩ H1 ∶ µ0 ≠ µ1 ,

(4)

Jo

where µ0 is the mean value of ∆x(t) in a time window [t−w, t], and µ1 is the mean

value in the next time window [t − w + τ, t + τ ]. The null hypothesis represents that there is no significant mean change, whereas the alternative hypothesis H1

means that a step change exists. If the null hypothesis H0 is rejected at a certain significance level, a step change is determined to exist in the process signal. 11

Otherwise, the null hypothesis is accepted, indicating no step change. In the normal drilling operation, the one-step difference ∆x does not show significant changes. It is assumed that ∆x(t) follows a Gaussian distribution, and thus the confidence interval can be obtained. The step change feature of x(t) is represented by b(t) ∈ {−1, 0, 1}. To reduce false alarms, a two-step difference, i.e., b(t) = −1 if

of

∆x(t) = x(t) − x(t − 2τ ), is also considered. Then, for a decreasing step change, ⎧ ⎪ ⎪ ⎪ ∆x(t) < l, ⎨ ⎪ ⎪ ⎪ ⎩ ∆x(t) < l,

ro

(5)

where l and l denote the lower confidence bounds of ∆x(t) and ∆x(t) at a certain

-p

significance level α, respectively. For an increasing step change, b(t) = 1 if

re

⎧ ⎪ ⎪ ⎪ ∆x(t) > u, ⎨ ⎪ ⎪ ⎪ ⎩ ∆x(t) > u,

(6)

lP

where u and u denote the upper confidence bounds of ∆x(t) and ∆x(t) at a certain significance level α, respectively. Otherwise, the step change feature signal

ur na

is taken as b(t) = 0.

Given all drilling process variables with fast step trends, a sample vector comprised by their step change features extracted in a short-time scale is T

(7)

Jo

b = [b1 (t), b2 (t), ⋯, bM1 (t)] ,

where M1 is the number of process variables with step change trends.

3.1.3. Extraction of slow varying features in a long-time scale For drilling process variables with slow varying trends, a long time scale with gradient sparse sampling is used to extract slow varying features. In practice, 12

samples closer to the evaluation point may have more significant influences to the variational trend. Therefore, the gradient sparse sampling is performed in a long time scale. Given a fixed time scale, recent samples are densely selected, while earlier samples are sparsely sampled. The trend feature in a long-time scale is extracted through the volatility analysis (Chen and Wang, 2017) and the least squares method. Given a time window

x(t) − x(t − τ ) . τ

ro

s(t) =

of

[t − γτ, t], a slope signal s(t) for x(t) is

max s(t1 ) −

t1 ∈[t−γτ,t]

min

t2 ∈[t−γτ,t]

s(t2 ).

(9)

re

v(t) =

-p

The volatility is then calculated as

(8)

sparse sampling as

lP

A sparse sampling segment Λ consisting of time stamps is obtained by gradient Λ = {t˜∣Λ∣ , ⋯, t˜1 , t˜0 },

ur na

⎧ ⎪ ⎪ t − ∑ki=1 (τ i) ⎪ t˜k = ⎨ ⎪ ⎪ t − γτ ⎪ ⎩

if ∑ki=1 i < γ − (k + 1),

(10) (11)

otherwise,

where k = 1, 2, ⋯, ∣ Λ ∣, t˜0 = t. Given the sparse sampling segment Λ, a linear fit

Jo

function is used to capture the trend of x(t) over the period [t − γτ, t], i.e., xˆ(t˜) = at˜ + e, t ∈ Λ,

(12)

where a and e are the slope and intercept parameters, respectively. The estimates

13

of a and e are obtained by minimizing the estimate errors, i.e., 2

min ∑ [x(t˜) − xˆ(t˜)] .

(13)

t˜∈Λ

The above trend extraction method in a linear way is reasonable owing to that the method is specifically designed for drilling variables with slow varying trends

of

and changes in signals of such variables are usually not significant over a given short time window, making the approximation by linear regression satisfactory in

ro

general.

For variables with slow varying trends, both the volatility v(t) and the slope

-p

a(t) are taken as the trend features. Then, two slow varying feature vectors are obtained, i.e.,

T

re

v = [v1 (t), v2 (t), ⋯, vM2 (t)] , T

(15)

lP

a = [a1 (t), a2 (t), ⋯, aM2 (t)] ,

(14)

where M2 is the number of process variables with slow varying features. 3.2. Probabilistic neural network based diagnosis modeling

ur na

In the area of fault diagnosis, either supervised (Porwik et al., 2016) or unsupervised (Liu et al., 2018b) methods are applicable depending on the availability of fault labels. As described in Section 2, the downhole incident diagnosis in this work is naturally a multi-class learning problem and thus can be solved using

Jo

supervised learning approaches. Due to the superior nonlinear modeling capability and the elite adaptive learning ability, the Artificial Neural Networks (ANNs) have been widely studied and applied for fault diagnosis (Liu et al., 2018a). The Probabilistic Neural Network is a classical ANN architecture proposed by D. F. Specht (Specht, 1990). It is a feedforward four-layer neural network on the basis of the radial basis neural network. With the incorporation of Bayesian inference 14

for the fusion of multiple features, this architecture shows superiority in solving multi-class learning problems with multi-feature inputs (Raman et al., 2017; Porwik et al., 2016). In this work, PNN is exploited for downhole incident diagnosis owing to two reasons: On one hand, the framework for the diagnosis of downhole incidents in this study involves different types of feature inputs, and PNN is capable of handling multi-feature inputs (Raman et al., 2017), making it

of

match the studied problem. On the other hand, diagnosing downhole incidents in real-time requires a timely calculation and decision, and PNN has a significan-

ro

t speed advantage compared to many other methods, such as back-propagation (Specht, 1990), making it meet the requirement on computational efficiency for

-p

online applications.

However, the classical structure of PNN only implements the input-to-output

re

mapping, without taking into account the features extracted from the input data. To solve this problem, inspired by the idea of broad leaning proposed in (Chen

lP

and Liu, 2018), the structure of a broad-PNN is proposed for downhole incident diagnosis. The architecture of the broad-PNN is shown in Fig. 3. Compared with the classic PNN structure, an additional part in the gray area is introduced to

ur na

expand the input data by adding some enhancement components. The original input of the broad-PNN are M dimensional vectors. After the feature extraction, enhancement components composed by feature vectors are obtained. Apart from the input layer, the structures of the pattern layer, summation layer, and output layer are the same as the classic PNN.

Jo

Based on the input matrix composed by the original sample vector x, en-

hancement components are obtained using the feature extraction methods in Section 3.1. In such a case, it is equivalent to add some new columns to the input matrix, and the new input matrix is composed by the x, b, v and a. The input layer is comprised by two groups of nodes, including original process variables 15

Enhancement components xE1 xE2

Feature extraction

xE3 噯

噯

xEk

› Lost circulation 噯

噯

x2

› Stuke pipe

噯噯 xM

噯

›

ro

噯

Downhole incident

of

x1

Overpulling

Summation layer Output layer

-p

Pattern layer

Input layer

Figure 3: Architecture of the broad-PNN. The feature extraction is conducted using the methods in Section 3.1

re

.

and all extracted trend features. The pattern layer is a layer of hidden neurons.

lP

The summation layer computes the sum of outputs from the pattern layer, and the output layer takes these sums to decide the final class based on the maxi˜ is comprised by mum likelihood decision. The expanded input sample vector x

ur na

original process signals and reconstructed trend features. The learning process of the broad-PNN is based on the Bayesian minimum risk decision criterion and the estimation of a Parzen window probability density function (Savchenko, 2013; Porwik et al., 2016). Using Bayes’ theorem, the conditional probability of a class

Jo

˜ is cj ∈ C given x

p (cj ∣˜ x) =

p (˜ x∣cj ) p (cj ) , p (˜ x)

(16)

where j = 0, 1, ⋯, ∣C∣, p (cj ) denotes the prior probability of cj , p (˜ x∣cj ) indicates

˜ given cj , and p (˜ the probability of observing x x) is termed the model evidence.

16

The Bayesian classifier is essentially a function given by yˆ = arg max p (cj ∣˜ x) ,

(17)

cj ∈C

˜. where yˆ is the predicted class for a feature vector x The diagram of the modeling process is shown in Fig. 4. Detailed calculations

of

in each layer are presented as follows:

Process signals x

ro

Normal

Lost circulation

Slow-varying features v and a

Broad- PNN based diagnosis model

-p

Drilling data

Stuke pipe Overpulling

re

Step-change features b

Drillstring washout

lP

Figure 4: Diagram of the modeling process.

˜ and delivers it to the patInput layer The input layer takes the feature vector x ˜ is composed by the drilling process variables tern layer. The input vector x

ur na

x and b, v, a, i.e.,

T

˜ = [xT , bT , vT , aT ] . x

(18)

Pattern layer The pattern layer is fully connected with the input layer. The number of nodes in the pattern layer is the same as that in the input layer.

Jo

The output of the ith node in the pattern layer corresponding to the jth class ˜ and the training is calculated based on the Euclidean distance between x input vector eij as T

(˜ x − eij ) (˜ x − eij ) ), Pij (˜ x) = exp (− 2 2σ 17

(19)

where σ is the smoothing factor, which is estimated in the training phase; eij is the ith training input for class cj . Summation layer The number of nodes in the summation layer is equal to the number of downhole conditions. Each node in the summation layer corresponds to a downhole incident. The jth node in the summation layer is given by gj

of

Sj = ∑ Pij (˜ x) ,

(20)

i=1

ro

where gj denotes the number of training instances for class cj .

Output layer By calculating the output of all neurons in the same class, the

-p

summation layer obtains the maximum likelihood that the input x ˜ is labeled with class cj . The output layer takes the downhole condition cj of the

re

maximum value in the summation layer as the output, i.e.,

lP

y = arg max cj ∈C

1 Sj . gj

(21)

ur na

3.3. Implementation procedures and performance assessment This work presents a novel downhole incident diagnosis method in a multi-time scale framework, which extracts trend features in multi-time scales and establishes a diagnosis model based on the broad-PNN. The diagram of the proposed diagnosis method in a multi-time scale framework is presented in Fig. 5. Procedures of the

Jo

proposed method are summarized as follows: ● Step 1: Data collection and preprocessing Historical data involving all relevant process variables is collected from a geo-

logical drilling process. To cope with imperfections of the raw data, data preprocessing is required to fill missing values, remove noises, and data normalization.

18

Drilling process data

Long time scale division

Hypothesis test based feature extraction

Volatility index and slope based feature extraction

ro

Short time scale division

of

Data preprocessing and normalization

-p

The broad-PNN based downhole incident diagnosis model

re

Maximum possible incident or normal condition

lP

Figure 5: Diagram of the proposed downhole incident diagnosis method in a multi-time scale framework.

For each process variable xo , the normalized signal x(t) is calculated as

ur na

x(t) =

(xo (t) − xomin ) , (xomax − xomin )

(22)

where xomax and xomin are the maximum and minimum of xo (t), respectively. Then, the range of x(t) is [-1,1]. Given a drilling process with M process variables, the T

sample vector at time instant t is x = [x1 (t) , x2 (t), ⋯, xM (t)] .

Jo

● Step 2: Extraction of trend features in a multi-time scale framework A hypothesis test is conducted to find out whether a process variable has a fast

step trend. Then, the method in Section 3.1.2 is applied to extract step change features in a short time scale. A step change feature vector b is constructed for M1 process variables with fast step change trends. In addition, the method in 19

Section 3.1.3 is exploited to extract slow varying features in a long time scale. The features are represented by two vectors, namely, v and a. The vector size for each of them is M2 . ● Step 3: Setup of experiments for the training of the incident diagnosis model To train a broad-PNN based diagnosis model, the data is stochastically partitioned for training and test in a standard way. The k-fold cross-validation is used

1 k ∑ Acck . k i=1

(23)

-p

Acc =

ro

of each fold is Acck , the overall accuracy Acc is obtained as

of

to verify the effectiveness of the proposed method. Assuming that the accuracy

● Step 4: Off-line training of the broad-PNN based incident diagnosis model

re

˜ is reassembled with the vector for original process variables The input vector x x, the feature vector for step change trends b, the feature vector of volatilities

lP

for slow varying trends v, and the feature vector of slopes for slow varying trends a. At each time instant t, the label, namely the downhole incident is known and denoted by y(t) ∈ C. In the training process, the smoothing parameter σ of

ur na

the broad-PNN model is determined to minimize the training error based on the metrics in eqns. (24-27).

● Step 5: On-line prediction to diagnose downhole incidents Given a new input vector x∗ , the new vector including both the original signals and extracted features is obtained after the data preprocessing in Step 1 and

Jo

˜ ∗ is used as the trend features extraction in Step 2. Then, the new input vector x input of the diagnosis model established in the Step 4. Its associated condition y is predicted by eqn. (21). ∎

To evaluate the performance of the proposed incident diagnosis method, met20

rics such as the accuracy, precision, recall, and F-Score, are used. The calculation of these metrics are based on the confusion matrix. There are four elements: nctpi represents the number of all instances that the ci condition is correctly classified as ci ; nctni represents the number of all instances that the non-ci condition is not classified as ci ; ncfip represents the number of all instances that the non-ci condition is falsely classified as ci ; ncfin represents the number of all instances that the ci

ro

Preci = nctpi /(nctpi + ncfip ) × 100%,

of

condition is not classified as ci . The formulas of the metrics are given as follows:

-p

Accci = (nctpi +nctni )/(nctpi +nctni + ncfip + ncfin ) × 100%,

(24) (25)

(26)

2 ∗ Preci ∗ Recci × 100%. Preci + Recci

(27)

Fsci =

re

Recci = nctpi /(nctpi +ncfin ) × 100%,

lP

In practice, it is impossible to eliminate all false positive ncfip or false negative ncfin . Thus, it is necessary to evaluate the performance of the proposed method

ur na

by different metrics.

4. Industrial Case Study

This section illustrates the effectiveness of the proposed method based on case studies with real industrial data collected from a drilling field in China (Wang,

Jo

2013). The collected drilling process data consisted of 175 subsets for 7 variables over different periods. The data was labeled with the drilling operation conditions, including the four aforementioned downhole incidents and the normal condition. There were 74 normal instances, 16 stuck pipe instances, 26 lost circulation instances, 28 drillstring washout instances, and 31 overpulling instances. Five-fold cross validation experiments were conducted to test the proposed method; in each 21

experiment, the data was randomly partitioned into two groups, including 140 instances for training and 35 instances for test. The data set exploited in the case study is relatively smaller compared to other applications, since the collection of drilling data is rather difficult. Especially, the scenarios associated with incident conditions could be very limited due to the low occurrence probability in real drilling production. Despite that, in the exploited data set, each condition was

of

associated with quite a few instances, making the collected data contain sufficient information required for the training of PNN. In the five-fold cross-validation, the

ro

data partition guaranteed at least two instances of each condition be included in each fold, so as to prevent the trained PNN from predicting unseen conditions.

-p

At first, trend features, including step changes and slow varying features, were extracted from normalized drilling signals. Fig. 6 (a) and (b) show the normalized

re

signals of SPP and MPV in the presence of a lost circulation incident, which occurred at t=12 min. SPP started to increase and MPV started to decrease

lP

almost at the same time. Fig. 6 (c) and (d) display the volatilities of SPP and MPV, respectively. It can be seen that the volatility of SPP was higher from t=13 min to t=34 min than other periods. Meanwhile, the volatility of MPV was

ur na

low from t=13 min to t=36 min. Fig. 6 (e) and (f) give the slopes of SPP and MPV, respectively. It can be seen that the slope of SPP was greater than 0 from t=13 min to t=23 min, and the slope of MPV was less than 0 from t=14 min to t=34 min. Thus, as observed from the volatilities and slopes for SPP and MPV, significant changes in the presence of the lost circulation were reflected by the

Jo

extracted trend features.

Fig. 7 (a) and (b) display the normalized signals of TRQ and WOB in the

presence of a drillstring washout incident, which occurred around t=15 min. It

can be seen that TRQ started to drop quickly at t=17 min, and meanwhile WOB changed significantly around t=15 min. Trend features were extracted in a short 22

(b) MPV Amplitude

Amplitude

(a) SPP 1 0 -1 0

5

10

15

20

25

30

1 0 -1

35

0

10

20

5

10

15

20

25

30

0

35

Amplitude

Amplitude

0.2 0.1 0 -0.1 5

10

15

20

30

40

30

40

0.5

Time (min) (e) SSPP

0

40

25

30

10

20

Time (min) (f) SMPV

0.1 0 -0.1 -0.2

35

0

of

0.2

ro

0.4

0

30

Time (min) (d) VMPV Amplitude

Amplitude

Time (min) (c) VSPP

0

10

20

Time (min)

-p

Time (min)

re

Figure 6: Examples of feature extraction for SPP and MPV that have slow varying features. (a) TRQ

Amplitude

0.5 0 -0.5 -1 5

10

15

20

25

ur na

0

lP

Amplitude

(b) WOB

1

1

0.5 0

-0.5 -1 0

30

5

10

-0.4 -0.6 -0.8

Jo

-1

0

5

10

15

20

25

25

30

20

25

30

1 0.8

Amplitude

Amplitude

-0.2

20

(d) CWOB

(c) CTRQ

0

15

Time (min)

Time (min)

0.6 0.4 0.2 0 0

30

5

10

15

Time (min)

Time (min)

Figure 7: Examples of feature extraction for TRQ and WOB that have step change features.

23

time scale for the two signals. Based on the hypothesis test to the mean change, step changes of the 2 signals were detected at time instants t=18 min and t=16 min, respectively. The two step change features are shown in Fig. 7(c) and (d). The original signal of WOB dropped to zero at t=17 min; this was owing to that the drilling was forced to suspend after the drilling worker was aware of the incident.

of

Next, a broad-PNN model was trained with 140 instances of training data with extracted trend features. In the training process, the influence of the smoothing

ro

factor σ was investigated. The trends of performance metrics versus σ are shown in Fig. 8. It can be seen that the performance metrics dropped given that σ

-p

was too small or too large, while high performance metrics were achieved with σ ∈ [0.07, 0.3]. More specifically, the maximum accuracy was found at σ = 0.2. In

re

subsequent experiments, the broad-PNN model was trained with σ = 0.2. 1

lP

0.9

0.7 0.6

ur na

Amplitude

0.8

0.5

Accuracy Precision Recall F-score

0.4 0.3 0.2

0.01

0.05

0.1

0.35

0.5

0.75

1.0

1.25

1.5

Smoothing factor

Jo

Figure 8: Trends of performance metrics versus σ of PNN.

To demonstrate the effectiveness of the proposed method, comparative ex-

periments were conducted using two methods, namely, the broad-PNN incident diagnosis based on both Original Process Signals (OPS) and Trend FeatureS (TFS), as well as the conventional PNN incident diagnosis based on only OPS. The 24

Predicted condition

True condition Stuke pipe

Lost circulation

Drillstring washout

Overpulling

Normal

74

4

3

0

1

Stuke pipe

1

12

0

0

0

Lost circulation

1

0

23

0

0

Drillstring washout

0

0

0

26

0

Overpulling

0

0

0

1

29

of

Normal

Predicted condition

True condition

ro

Figure 9: Confusion matrix using the broad-PNN diagnosis based on both the Original Process Signals (OPS) and Trend FeatureS (TFS).

Stuke pipe

Lost circulation

Drillstring washout

Overpulling

Normal

72

6

3

1

0

Stuke pipe

3

8

0

1

0

Lost circulation

0

Drillstring washout

1

re 0

23

0

0

2

0

20

0

0

5

30

lP

Overpulling

0

-p

Normal

0

ur na

Figure 10: Confusion matrix using the conventional PNN diagnosis based on only Original Process Signals (OPS).

confusion matrices obtained by the two methods are shown in Figs. 9 and 10. The columns of each confusion matrix correspond to true conditions, while the rows correspond to predicted conditions. For a real condition ci , the ith diagonal

Jo

element nctpi represents the number of instances that ci is correctly predicted, while the non-diagonal elements in the ith column denote the ci is incorrectly classified into other conditions. Comparing the diagonal sums in Figs. 9 and 10, the number of correctly predicted instances using the broad-PNN diagnosis method is larger. Table 2 summarizes average values of the four metrics obtained from the 5-fold

25

Table 2: Results using the broad PNN diagnosis based on Original Process Signals (OPS) and Trend FeatureS (TFS) (%).

Normal

Accuracy Precision Recall F-Score

94.29 97.37 90.24 93.70

Stuke pipe 97.14 75.00 92.31 82.76

Lost circulation 97.71 88.46 95.81 95.91

Drillstring washout 99.43 96.29 100.00 98.11

Overpulling 98.86 96.67 96.67 96.67

of

Metrics

Table 3: Results using the conventional PNN diagnosis based on only Original Process Signals (OPS) (%).

92.00 94.74 87.80 89.66

Lost circulation 98.29 88.46 100.00 90.69

Drillstring washout 94.29 74.07 86.96 80.00

Overpulling

ro

Accuracy Precision Recall F-Score

Stuke pipe 93.14 50.00 66.67 57.14

-p

Normal

97.14 100.00 85.71 92.31

re

Metrics

cross validation using the proposed broad-PNN incident diagnosis based on Orig-

lP

inal Process Signals (OPS) and Trend FeatureS (TFS). It can be seen that almost all calculated metrics were over 90% except for three elements. Table 3 gives average values of the four metrics obtained using the conventional PNN incident

ur na

diagnosis based on only Original Process Signals (OPS). It can be observed from Tables 2 and 3 that the proposed method outperformed the conventional PNN incident diagnosis based on only original process signals in most metrics. For instance, the precision, recall, and F-score for the diagnosis of stuke pipe incidents were improved by nearly 30% overall. Further, Table 4 compares the accuracies

Jo

obtained from the 5-fold cross-validation. It is obvious that the average accuracy increased by 7.2% from 87.43% to 93.71%. Therefore, it can be concluded that expanding the input space of the PNN by feature extraction in multi-time scales helped to improve the performance of downhole incident diagnosis. To further investigate the influence of feature extraction in downhole incident 26

Table 4: Accuracies of 5-fold cross-validation based on OPS and TFS(%).

Method The proposed method The contentional method

Fold 1 82.86 80.00

Fold 2 91.43 91.43

Fold 3 Fold 4 100.00 97.14 91.43 80.00

Fold 5 97.14 94.29

Average 93.71 87.43

Table 5: Performance metrics of the proposed broad-PNN diagnosis based on different inputs (%).

Precision 91.87 88.96 91.99

Recall 93.48 90.39 95.67

F-Score 92.66 89.66 93.79

of

Accuracy 92.57 92.00 93.71

ro

Inputs OPS and TFS in STS OPS and TFS in LTS OPS and TFS in MTS

diagnosis, more experiments were conducted to test the proposed broad-PNN in-

-p

cident diagnosis method with Trend FeatureS (TFS) extracted in different time scales, including the Short Time Scale (STS), Long Time Scale (LTS), and Multi-

re

Time Scale(MTS). The average precision, accuracy, recall, and F-score were presented in Table 5. It can be seen that the method using OPS and TFS in

lP

MTS achieved best performances on the four metrics while the method using OPS and TFS in LTS had worst performances on these metrics. This is because that the features extracted in LTS may lose certain information for variables with

ur na

fast step variational trends and thus may compromise the diagnosis performance. By contrast, the extraction of features in MTS is appropriate since the original process signals are naturally categorized into two groups depending on how fast they change with time. Furthermore, the average accuracy, precision, recall, and F-score obtained using the conventional PNN diagnosis method based on only

Jo

OPS were 87.43%, 83.63%, 86.78%, and 85.17%, respectively. It is obvious that the proposed method with OPS and TFS in MTS significantly outperformed the conventional PNN diagnosis on all the four metrics. To demonstrate the superiority of the proposed method, comparative experiments were conducted to compare with classical classification methods presented 27

in (Kuncan et al., 2019). Table 6 gives the downhole incident diagnosis results using different methods, including BP (Back Propagation neural networks), ELM (Extreme Learning Machine), k-NN (k-Nearest Neighbor), SVM (Support Vector Machine), BayesNet (Bayesian Network), and the proposed method. All these methods took both drilling signals and extracted trend features as the inputs. By comparing the calculated metrics in each column, it can be observed that the

of

proposed method outperformed all the other methods almost on every metric.

Even though the precision of the proposed method was slightly lower than that

ro

of the BayesNet, the proposed method achieved much better performances on the

other three metrics. According to the downhole incident diagnosis results, the

-p

superiority of the proposed method is significant compared to the other classical classification methods.

re

Table 6: Downhole incident diagnosis results based on different classification methods (%).

Precision 86.36 91.54 87.36 86.31 92.03 91.99

ur na

lP

Method Accuracy BP 89.14 SVM 92.00 ELM 89.14 k-NN 89.71 BayesNet 88.00 Proposed method 93.71

Recall 92.33 93.38 88.23 88.14 88.14 95.67

F-Score 88.96 92.27 87.67 87.06 89.83 93.79

5. Conclusion

Jo

This work proposes a systematic method to diagnose downhole incidents for geological drilling processes based on a broad probabilistic neural network in a multi-time scale framework. First, trend features, including step change and slow varying features, are extracted in different time scales. Then, a broad-PNN model is established based on expanded inputs, including both the drilling signals and trend features. An industrial case study with real drilling data was presented to 28

demonstrate the validity of the proposed method. According to the results from the five-fold cross validation, the proposed method outperformed the conventional PNN based on only drilling signals in most metrics. The accuracy of the downhole incident diagnosis using the proposed method was as high as 93.71%. It was concluded that expanding the input space of PNN by feature extraction helped to improve the performance of downhole incident diagnosis. More experiments

of

were conducted to investigate the influence of feature extraction. It was found

that the method with trend features extracted in multi-time scales achieved much

ro

better performance compared to that with trend features extracted in one uniform time scale, either the short one or the long one. The superiority of the proposed

-p

method was demonstrated by comparative experiments. It was observed from the calculated metrics that the proposed method outperformed the other five clas-

re

sical methods, including Back Propagation neural networks, Extreme Learning Machine, k-Nearest Neighbor, and Support Vector Machine. In conclusion, the

lP

proposed method provides an effective way to diagnose downhole incidents in geological drilling, and shows strong practicability and superiority compared to other classical methods. There are also some future opportunities to extend this study.

ur na

For example, some drilling processes may have strong nonlinear characteristics; then how to extract trend features in a nonlinear way is a problem should be solved prior to diagnosis. Further, to help drilling workers to get aware of worst situations when a downhole incident occurs, it also deserves exploration how to achieve monitoring for drilling processes in an unsupervised way, such as that in

Jo

(Liu et al., 2018b), and get the upper and lower control limits. Acknowledgments This work was supported by the National Natural Science Foundation of China under Grants 61733016 and 61903345, the Hubei Provincial Technical Innovation 29

Major Project under Grant 2018AAA035, the National Key R&D Program of China under Grant 2018YFC0603405, the Hubei Provincial Natural Science Foundation under Grant 2019CFB251, the 111 project under Grant B17040, and the Fundamental Research Funds for the Central Universities under Grant CUGCJ1812. The authors are grateful to the Hubei Geological Bureau Eighth Geological Brigade for their contributions to this project. They provided industrial data and

of

a lot of useful suggestions for this project.

ro

Abimbola, M., Khan, F., 2016. Development of an integrated tool for risk analysis

of drilling operations. Process Safety and Environmental Protection 102, 421–

-p

430.

Abimbola, M., Khan, F., Khakzad, N., Butt, S., 2015. Safety and risk analysis

re

of managed pressure drilling operation using bayesian network. Safety Science 76, 133–144.

lP

Adedigba, S.A., Oloruntobi, O., Khan, F., Butt, S., 2018. Data-driven dynamic risk analysis of offshore drilling operations. Journal of Petroleum Science and

ur na

Engineering 165, 444–452.

Al-Hameedi, A.T.T., Alkinani, H.H., Dunn-Norman, S., Flori, R.E., Hilgedick, S.A., 2018. Real-time lost circulation estimation and mitigation. Egyptian Journal of Petroleum 27, 1227–1234.

Jo

Albdiry, M., Almensory, M., 2016. Failure analysis of drillstring in petroleum industry: A review. Engineering Failure Analysis 65, 74–85.

Bhandari, J., Abbassi, R., Garaniya, V., Khan, F., 2015. Risk analysis of deepwater drilling operations using bayesian network. Journal of Loss Prevention in the Process Industries 38, 11–23. 30

Chen, C.L.P., Liu, Z., 2018. Broad learning system: An effective and efficient incremental learning system without the need for deep architecture. IEEE Transactions on Neural Networks and Learning Systems 29, 10–24. Chen, K., Wang, J., 2017. Design of multivariate alarm systems based on online calculation of variational directions. Chemical Engineering Research and Design

of

122, 11–21. Gan, C., Cao, W., Wu, M., Liu, K.Z., Chen, X., Hu, Y., Ning, F., 2019. Two-level

ro

intelligent modeling method for the rate of penetration in complex geological drilling process. Applied Soft Computing 80, 592–602.

-p

Gao, X., Li, H., Wang, Y., Tao, C., Xin, Z., Lei, Z., 2018. Fault detection in managed pressure drilling using slow feature analysis. IEEE Access 6, 34262–

re

34271.

lP

Godhavn, J.M., 2010. Control requirements for automatic managed pressure drilling system. SPE Drilling & Completion 25, 336–345. He, R., Li, X., Chen, G., Wang, Y., Jiang, S., Zhi, C., 2018. A quantitative risk

ur na

analysis model considering uncertain information. Process Safety and Environmental Protection 118, 361–370. Kuncan, 2019.

M.,

Kaplan,

K.,

Minaz,

M.R.,

Kaya,

Y.,

Ertun¸c,

H.M.,

A novel feature extraction method for bearing fault classifica-

Jo

tion with one dimensional ternary patterns.

ISA Transactions, http-

s://doi.org/10.1016/j.isatra.2019.11.006.

Liu, R., Hasan, A.R., Ahluwalia, A., Mannan, M.S., 2016. Well specific oil discharge risk assessment by a dynamic blowout simulation tool. Process Safety and Environmental Protection 103, 183–191. 31

Liu, R., Yang, B., Zio, E., Chen, X., 2018a. Artificial intelligence for fault diagnosis of rotating machinery: A review. Mechanical Systems and Signal Processing 108, 33–47. Liu, Y., Liu, B., Zhao, X., Xie, M., 2018b. A mixture of variational canonical correlation analysis for nonlinear and quality-relevant process monitoring. IEEE

of

Transactions on Industrial Electronics 65, 6478–6486. Moazzeni, A., Nabaei, M., Jegarluei, S.G., 2012. Decision making for reduction of

ro

nonproductive time through an integrated lost circulation prediction. Petroleum Science and Technology 30, 2097–2107.

-p

Nayeem, A.A., Venkatesan, R., Khan, F., 2016. Monitoring of down-hole parameters for early kick detection. Journal of Loss Prevention in the Process

re

Industries 40, 43–54.

lP

Nikoofard, A., Aarsnes, U.J.F., Johansen, T.A., Kaasa, G., 2017. State and parameter estimation of a drift-flux model for underbalanced drilling operations. IEEE Transactions on Control Systems Technology 25, 2000–2009.

ur na

Perez, P., Tan, H., 2018. Accident precursor probabilistic method (APPM) for modeling and assessing risk of offshore drilling blowouts − A theoretical microscale application. Safety Science 105, 238–254. Porwik, P., Doroz, R., Orczyk, T., 2016. Signatures verification based on PNN

Jo

classifier optimised by PSO algorithm. Pattern Recognition 60, 998–1014.

Raman, M.G., Somu, N., Kirthivasan, K., Sriram, V.S., 2017. A hypergraph and arithmetic residue-based probabilistic neural network for classification in intrusion detection systems. Neural Networks 92, 89–97.

32

Savchenko, A., 2013. Probabilistic neural network with homogeneity testing in recognition of discrete patterns set. Neural Networks 46, 227–241. Sheremetov, L., Batyrshin, I., Filatov, D., Martinez, J., Rodriguez, H., 2008. Fuzzy expert system for solving lost circulation problem. Applied Soft Computing 8, 14–29.

of

Skalle, P., Aamodt, A., Gundersen, O.E., 2013. Detection of Symptoms for Re-

vealing Causes Leading to Drilling Failures. SPE Drilling & Completion 28,

ro

182–193.

-p

Specht, D.F., 1990. Probabilistic neural networks. Neural networks 3, 109–118. Sule, I., Imtiaz, S., Khan, F., Butt, S., 2019. Risk analysis of well blowout scenarios

Engineering 182, 106296.

re

during managed pressure drilling operation. Journal of Petroleum Science and

lP

Sun, X., Sun, B., Zhang, S., Wang, Z., Gao, Y., Li, H., 2018a. A new pattern recognition model for gas kick diagnosis in deepwater drilling. Journal of

ur na

Petroleum Science and Engineering 167, 418–425. Sun, Z., 2006. Drilling anomaly prediction technology. Petroleum Industry Press. Sun, Z.Y., Zhou, J.L., Gan, L.F., 2018b. Safety assessment in oil drilling work system based on empirical study and analytic network process. Safety Science

Jo

105, 86–97.

Tamim, N., Laboureur, D.M., Hasan, A.R., Mannan, M.S., 2019. Developing leading indicators-based decision support algorithms and probabilistic models using bayesian network to predict kicks while drilling. Process Safety and Environmental Protection 121, 239–246. 33

Tamim, N., Laboureur, D.M., Mentzer, R.A., Hasan, A.R., Mannan, M.S., 2017. A framework for developing leading indicators for offshore drillwell blowout incidents. Process Safety and Environmental Protection 106, 256–262. Wang, J., 2013. Study on the early warning technology of drilling engineering accidents. Ph.D. thesis. China University of Geosciences.

of

Wang, Q., 2010. Engineering Logging Practical Atlas. Petroleum Industry Press. Willersrud, A., Blanke, M., Imsland, L., Pavlov, A., 2015. Fault diagnosis of down-

ro

hole drilling incidents using adaptive observers and statistical change detection.

-p

Journal of Process Control 30, 90–103.

Willersrud, A., Imsland, L., Pavlov, A., Kaasa, G.O., 2013. A framework for fault diagnosis in managed pressure drilling applied to flow-loop data. IFAC

re

Proceedings Volumes 46, 625–630.

lP

Wu, S., Zhang, L., Fan, J., Zhou, Y., 2019. Dynamic risk analysis of hydrogen sulfide leakage for offshore natural gas wells in MPD phases. Process Safety and Environmental Protection 122, 339–351.

ur na

Zhang, L., Zhang, X., Hu, J., Liu, H., 2018a. A comprehensive method for safety management of a complex pump injection system used for shale-gas well fracturing. Process Safety and Environmental Protection 120, 370–387. Zhang, X., Zhang, L., Hu, J., 2018b. Real-time diagnosis and alarm of down-hole

Jo

incidents in the shale-gas well fracturing process. Process Safety and Environmental Protection 116, 243–253.

Zhou, J., Stamnes, .N., Aamo, O.M., Kaasa, G., 2011. Switched control for pressure regulation and kick attenuation in a managed pressure drilling system. IEEE Transactions on Control Systems Technology 19, 337–350. 34