Valuable Cuttings-Based Petrophysic Analysis Successfully Reduces Drilling Risk in HPHT Formations

2021 ◽  
Author(s):  
Jianlin Chen ◽  
Yanhua Yao ◽  
Yingbiao Liu ◽  
Zhaofei Wang ◽  
Craig Collier ◽  
...  
Keyword(s):  
Real Time ◽  
Fluid Loss ◽  
Bottom Line ◽  
Rock Density ◽  
Drilling Parameters ◽  
Drilling Operations ◽  
Analysis Application ◽  
Time Basis ◽  
Compressional Strength ◽  
Drilling Conditions

Abstract Cuttings data has always been neglected or forgotten as a source of information by many operators. In some areas, it is even common practice to throw away cuttings in order to reduce cost. However, cuttings data can yield a great amout of information to provide great value and support to drilling operations, as well as reduce potential downhole risks. This was evident in wells drilled in remote Western regions of China, where wells typically have high temperature high pressure (HTHP) formations with a true vertical depth ranging between 4000-7000 meters and target formation temperature between 150-160 degrees Celcius. Due to severe drilling conditions, the measurement tools of Logging While Drilling (LWD) and Measured While Drilling (MWD) are at high risk of running into holes. Even due to the high formations’ temperatures is over the bottom line of LWD and MWD tools, the sensors of LWD and MWD cannot work efficiently in such circumstances, increasing the drilling risk and expense. Thus, "blind" drilling is the most reasonable economical choice for local operators. Without sufficient real-time formations’ information, the drilling uncertainties dramatically increase. The fluid loss, pipe stuck, as well as drilling bit damages frequently occur. Currently, there is no successful well that accesses to the target reservoir. The data from the wireline logs and cores cannot be available, as the well is the first exploration well in the block; however, during drilling, only drill cuttings are available for peoples. The creative cutting-based petrophysics models are built for the formation analysis that is able to provide rock density, cuttings gamma, Delta Time of Compressional Acoustic (DTC), Unconfined Compressional Strength (UCS) Index, Caliper Index, Brittleness Index, and Hydrocarbon Index from the cuttings samples at the wellsite on a near-real-time basis. This data can help people quantitatively and qualitatively evaluate the downhole formations on a near-real-time basis and can help people to make a more reasonable decision, and therefore, reduce the drilling risk within a controlled level. The authors provide the several cases to study the cutting models into drilling events, and proves the models are consistent with log and core data, and match the drilling parameters and like ROP, and pumping pressure, as well as torque, and bit performance. LWD and MWD are unable to run into the hole due to high formation pressure and extreme risky hole. The field portable XRF instrument is applied, and the mineralogy and elements input into the models. The cuttings petrophysics analysis application can provide the valuable information for drilling engineers to drill the wells to TD.

From Science to Practice: Improving ROP by Utilizing a Cloud-Based Machine-Learning Solution in Real-Time Drilling Operations

2021 ◽  
Author(s):  
Kriti Singh ◽  
Sai Yalamarty ◽  
Curtis Cheatham ◽  
Khoa Tran ◽  
Greg McDonald
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Situational Awareness ◽  
Field Tests ◽  
Field Trials ◽  
Permian Basin ◽  
Lateral Vibrations ◽  
Drilling Parameters ◽  
Drilling Conditions ◽  
Performance Results

Abstract This paper is a follow up to the URTeC (2019-343) publication where the training of a Machine Learning (ML) model to predict rate of penetration (ROP) is described. The ML model gathers recent drilling parameters and approximates drilling conditions downhole to predict ROP. In real time, the model is run through an optimization sweep by adjusting parameters which can be controlled by the driller. The optimal drilling parameters and modeled ROP are then displayed for the driller to utilize. The ML model was successfully deployed and tested in real time in collaboration with leading shale operators in the Permian Basin. The testing phase was split in two parts, preliminary field tests and trials of the end-product. The key learnings from preliminary field tests were used to develop an integrated driller's dashboard with optimal drilling parameters recommendations and situational awareness tools for high dysfunction and procedural compliance which was used for designed trials. The results of field trials are discussed where subject well ROP was improved between 19-33% when comparing against observation/control footage. The overall ROP on subject wells was also compared against offset wells with similar target formations, BHAs, and wellbore trajectories. In those comparisons against qualified offsets, ROP was improved by as little as 5% and as much as 33%. In addition to comparing ROP performance, results from post-run data analysis are also presented. Detailed drilling data analytics were performed to check if using the recommendations during the trial caused any detrimental effects such as divergence in directional trends or high lateral or axial vibrations. The results from this analysis indicate that the measured downhole axial and lateral vibrations were in the safe zone. Also, no significant deviations in rotary trends were observed.

Real-Time Prediction of Mud Motor Failure Using Surface Sensor Data Features and Trends

2021 ◽  
Author(s):  
Tesleem Lawal ◽  
Pradeepkumar Ashok ◽  
Eric van Oort ◽  
Dandan Zheng ◽  
Matthew Isbell
Keyword(s):  
Real Time ◽  
Cost Optimization ◽  
Damage Index ◽  
Sensor Data ◽  
Drilling Parameters ◽  
Wear And Tear ◽  
Drilling Operations ◽  
Downhole Measurements ◽  
The Impact ◽  
Motor Failure

AbstractMud motor failure is a significant contributor to non-productive time in lower-cost land drilling operations, e.g. in North America. Typically, motor failure prevention methodologies range from re-designing or performing sophisticated analytical modeling of the motor power section, to modeling motor performance using high-frequency downhole measurements. In this paper, we present data analytics methods to detect and predict motor failures ahead of time using primarily surface drilling measurements.We studied critical drilling and non-drilling events as applicable to motor failure. The impacts of mud motor stalls and drill-off times were investigated during on-bottom drilling. For the off-bottom analysis, the impact of variations in connection practices (pick up practices, time spent backreaming, and time spent exposing the tools to damaging vibrations) was investigated. The relative importance of the various features found to be relevant was calculated and incorporated into a real-time mud motor damage index.A historical drilling dataset, consisting of surface data collected from 45 motor runs in lateral hole sections of unconventional shale wells drilled in early to mid-2019, was used in this study. These motor runs contained a mix of failure and non-failure cases. The model was found to accurately predict motor failure due to motor wear and tear. Generally, the higher the magnitude of the impact stalls experienced by the mud motor, the greater the probability of eventual failure. Variations in connection practices were found not to be a major wear-and-tear factor. However, it was found that connection practices varied significantly and were often driller-dependent.The overall result shows that simple surface drilling parameters can be used to predict mud motor failure. Hence, the value derived from surface sensor information for mud motor management can be maximized without the need to run more costly downhole sensors. In addition to this cost optimization, drillers can now monitor motor degradation in real-time using the new mud motor index described here.

Fracture Pressure Prediction Using Surface Drilling Parameters by Artificial Intelligence Techniques

2020 ◽  
Vol 143 (3) ◽  
Author(s):  
Abdulmalek Ahmed ◽  
Salaheldin Elkatatny ◽  
Abdulwahab Ali
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Optimal Model ◽  
Time Data ◽  
Fracture Pressure ◽  
Drilling Parameters ◽  
Drilling Operations

Abstract Several correlations are available to determine the fracture pressure, a vital property of a well, which is essential in the design of the drilling operations and preventing problems. Some of these correlations are based on the rock and formation characteristics, and others are based on log data. In this study, five artificial intelligence (AI) techniques predicting fracture pressure were developed and compared with the existing empirical correlations to select the optimal model. Real-time data of surface drilling parameters from one well were obtained using real-time drilling sensors. The five employed methods of AI are functional networks (FN), artificial neural networks (ANN), support vector machine (SVM), radial basis function (RBF), and fuzzy logic (FL). More than 3990 datasets were used to build the five AI models by dividing the data into training and testing sets. A comparison between the results of the five AI techniques and the empirical fracture correlations, such as the Eaton model, Matthews and Kelly model, and Pennebaker model, was also performed. The results reveal that AI techniques outperform the three fracture pressure correlations based on their high accuracy, represented by the low average absolute percentage error (AAPE) and a high coefficient of determination (R2). Compared with empirical models, the AI techniques have the advantage of requiring less data, only surface drilling parameters, which can be conveniently obtained from any well. Additionally, a new fracture pressure correlation was developed based on ANN, which predicts the fracture pressure with high precision (R2 = 0.99 and AAPE = 0.094%).

scholarly journals Estimation of Caving volumetric-flow using the ultrasonic Doppler method

2018 ◽  
Vol 8 (1) ◽  
pp. 61-66
Author(s):  
Jenny-Mabel Carvajal-Jiménez
Keyword(s):  
Real Time ◽  
Flow Line ◽  
Wellbore Stability ◽  
Drilling Mud ◽  
Time Operation ◽  
Major Interest ◽  
Real Time Operation ◽  
Invasive Method ◽  
Time Basis ◽  
Drilling Conditions

Estimating the volumetric flow of cuttings and cavings that are extracted and transported by the drilling mud into the flow line while drilling a well is of major interest to for drillers so that they can understand the drilling conditions and maintain the wellbore stability. In this paper, a new method to estimate the volumetric flow of cavings via the Doppler effect is proposed. The proposed method is a non-invasive method that uses two piezo-electric acoustic sensors located on the flow line, one acting as an emitter and the other acting as a receiver. The system device estimates cavings and cuttings by measuring the mud and solids flow on a real-time basis. Results obtained at a laboratory experimental level reflected a maximum volumetric-flow error of 10.5% for small-sized cavings and 34,7% for large-sized cavings. According to those results, the method may be suitable for estimating cavingvolumetric-flow with acoustical techniques at the flow line while drilling and it might be used as a real-time operation method to evaluate wellbore stability.

Turbodrill Bottomhole Assembly and Real-Time Data Improve Through-Tubing Coiled-Tubing Drilling Operations in Sour Gas Well

2015 ◽  
Author(s):  
A. Ebrahimi ◽  
P. J. Schermer ◽  
W. Jelinek ◽  
D. Pommier ◽  
S. Pfeil ◽  
...  
Keyword(s):  
Real Time ◽  
Time Data ◽  
Gas Well ◽  
Coiled Tubing ◽  
Real Time Data ◽  
Drilling Operations ◽  
Sour Gas

scholarly journals A Force Sensorless Method for CFRP/Ti Stack Interface Detection during Robotic Orbital Drilling Operations

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Qiang Fang ◽  
Ze-Min Pan ◽  
Bing Han ◽  
Shao-Hua Fei ◽  
Guan-Hua Xu ◽  
...  
Keyword(s):  
Cutting Force ◽  
Thrust Force ◽  
Cutting Parameters ◽  
Force Model ◽  
Drilling Process ◽  
Reinforced Plastics ◽  
Orbital Drilling ◽  
Drilling Parameters ◽  
Drilling Operations ◽  
Machining Properties

Drilling carbon fiber reinforced plastics and titanium (CFRP/Ti) stacks is one of the most important activities in aircraft assembly. It is favorable to use different drilling parameters for each layer due to their dissimilar machining properties. However, large aircraft parts with changing profiles lead to variation of thickness along the profiles, which makes it challenging to adapt the cutting parameters for different materials being drilled. This paper proposes a force sensorless method based on cutting force observer for monitoring the thrust force and identifying the drilling material during the drilling process. The cutting force observer, which is the combination of an adaptive disturbance observer and friction force model, is used to estimate the thrust force. An in-process algorithm is developed to monitor the variation of the thrust force for detecting the stack interface between the CFRP and titanium materials. Robotic orbital drilling experiments have been conducted on CFRP/Ti stacks. The estimate error of the cutting force observer was less than 13%, and the stack interface was detected in 0.25 s (or 0.05 mm) before or after the tool transited it. The results show that the proposed method can successfully detect the CFRP/Ti stack interface for the cutting parameters adaptation.

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