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Abstract Up to now, authors have proposed several drilling rate models. Bourgoyne and Young model represents a general relation between penetration rate and some drilling variables. In this model, there are eight constants, which are dependent to the formation type and must be found from previous drilling data. Bourgoyne and Young recommended multiple regression method to solve these eight unknowns but this method is limited to the number of data points and recommended ranges for drilling parameters. In this research, by writing a computer program, three other mathematical methods were applied on nine wells data of Khangiran gas field to find each formation constants. Results of applied four methods were compared to each other and it was found that trust-region method is the best mathematical method to find Bourgoyne and Young penetration model constants and in many situations, results of other three methods were not meaningful. Since, this method has not limitations of recommended method, it can be applied easily to predict penetration rate when e few data points are present and when drilling parameters are not in the recommended ranges. Introduction Penetration rate prediction is always one the most important issue among drilling engineers, because it makes the possibility to select optimum drilling parameters to achieve the minimum cost per foot. Scientists have suggested several mathematical equations to relate the drilling rate and major controllable and uncontrollable drilling variables. However, because a large number of drilling variables affect the drilling rate and there are some relationships between themselves, a model with 100% accuracy has not been proposed yet (1, 2). Perhaps the most complete drilling rate model, used for roller cone bits is Bourgoyne and Young's model (1). Eight functions are used in their equation to model the effect of most important drilling variables. In this model, there are some unknown coefficients that must be chosen based on local drilling conditions. In fact, the accuracy of this model is dependent to the constants quantities and therefore, applying a reliable mathematical technique to compute these coefficients is on the great importance. In this research, first, we will discuss in detail about Bourgoyne and Young drilling rate model. Then, the mathematical methods to find unknown coefficients of this model will be illustrated. Next, using the most reliable mathematical technique, unknowns of Khangiran gas field formations will be estimated.
- Geology > Geological Subdiscipline (0.69)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock (0.32)
- Well Drilling > Drilling Operations (1.00)
- Well Completion > Completion Installation and Operations > Perforating (0.54)
- Well Drilling > Drill Bits > Bit design (0.34)
Real-time Prediction of Rate of Penetration During Drilling Operation In Oil And Gas Wells
Jahanbakhshi, R. (Young Researchers Club, Science and Research Branch, Islamic Azad University) | Keshavarzi, R. (Young Researchers Club, Science and Research Branch, Islamic Azad University) | Jafarnezhad, A. (Department of Petroleum, Omidieh Branch, Islamic Azad University)
ABSTRACT: The researchers in the drilling engineering fields are always looking for the prediction of unexpected events and optimizing the related parameters. Predicting the Rate of Penetration (ROP) is of a great attention for drilling engineers due to its effect on the optimization of various parameters that leads to reduction of the costs. Artificial neural network (ANN) has an efficient capability of combining different parameters to predict different situations. According to ANN structure, it can get the effective parameters as the inputs to predict and evaluate the value of the target parameter(s) as an output. Since formation type and rock mechanical properties, hydraulics, bit type and its properties, weight on the bit and rotary speed are the most important parameters that affect ROP, they have been considered as the input parameters to predict ROP. In this study, ROP has been investigated and predicted in one of Southern Iranian oilfields through an ANN model. Finally, ROP has been predicted prosperously by the developed ANN which has been checked with the field measurements of drilled wells. The results indicate the efficiency of ANN in this field which can be used in drilling planning and real-time operation of any oil and gas wells in the related field that can result in costs reduction. 1. INTRODUCTION Analyzing real-time data is an efficient tool for improving drilling operation which leads to reduction in drilling costs. For developing advanced real-time analysis, rate of penetration (ROP) prediction is always one the most key aspects among drilling engineers, because it makes the possibility to optimize drilling parameters to achieve the minimum cost per foot Drilling optimization using ROP models is done by changing the drilling parameters and/or bit design to find the optimum drilling scenario for an entire bit run [1, 2].
- Asia > Middle East > Iran (0.28)
- North America > United States > California (0.28)
A Study of the Applicability of Bourgoyne & Young ROP Model and Fitting Reliability through Regression
Kutas, D. T. (Montanuniversitaet Leoben - MUL) | Nascimento, A. (Universidade Estadual Paulista - UNESP / PRH48) | Elmgerbi, A. M. (Montanuniversitaet Leoben - MUL) | Roohi, A. (Montanuniversitaet Leoben - MUL) | Prohaska, M. (Montanuniversitaet Leoben - MUL) | Thonhauser, G. (Montanuniversitaet Leoben - MUL) | Mathias, M. H. (Universidade Estadual Paulista - UNESP / PRH48)
Abstract Pre-salt layers has long-term exploration and production possibilities, however the properties of such layers are highly challenging for exploration (i.e. these carbonate layers are highly abrasive, located deeper than 5000 m, have generally low permeability) and production (i.e. pre-salt layers are located in harsh oceanic conditions, hundreds of km offshore). These conditions mean increased exploration technological and economic difficulties. One of the partial solutions to decrease costs is to reduce the drilling operations time. Rate of penetration (ROP) has a significant effect on the overall drilling time, driving ROP modeling and optimization to be a viable solution for reducing drilling operations time in such environment. Several mathematical ROP models were developed in the last five decades in the petroleum industry, departing from rather simple but less reliable R-W-N (drilling-rate, weight-on-bit and rotary-speed) equations until the arrival to a comprehensive and complex approach: Bourgoyne and Young ROP Model (BYM) which was first published in 1974. The paper explains the equation, how it is applied in terms of ROP modeling, identifies the main drilling parameters driving each sub-functions, and introduces how they were developed. The paper base itself on the sub-functions of the equation, explains the normalization factors which have significant influence on the model, and also introduces simulations which aim to understand the approach by applying the equation in a pre-salt layer case study. Knowing that, the original publication was introduced in 1974, this paper also aims to identify rooms for improvement and/or alternate the sub-functions to match actual field data better than with the originally given drillability coefficient recommended boundaries stated in the first publication. This is accomplished through a real-world practical application of this ROP model in a pre-salt layer. The paper also assesses the limitation in terms of the applicability of this complex model for ROP analysis and optimization in such carbonate layers.
- South America > Brazil (0.96)
- North America > United States > Louisiana (0.28)
- North America > United States > California (0.28)
- South America > Brazil > Brazil > South Atlantic Ocean > Santos Basin (0.94)
- South America > Brazil > Campos Basin (0.93)
- Well Drilling > Drillstring Design > Torque and drag analysis (1.00)
- Well Drilling > Drilling Operations (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Exploration, development, structural geology (1.00)
- Well Drilling > Drilling Fluids and Materials > Drilling fluid management & disposal (0.93)
Evaluation of Derived Controllable Variables for Predicting Rop Using Artificial Intelligence in Autonomous Downhole Rotary Drilling System
Amadi, Kingsley Williams (Australian College of Kuwait) | Iyalla, Ibiye (Robert Gordon University Aberdeen) | Liu, Yang (University of Exeter, England) | Alsaba, Mortadha (Australian College of Kuwait) | Kuten, Durdica (Australian College of Kuwait)
Abstract Fossil fuel energy dominate the world energy mix and plays a fundamental role in our economy and lifestyle. Drilling of wellbore is the only proven method to extract the hydrocarbon reserves, an operation which is both highly hazardous and capital intensive. To optimize the drilling operations, developing a high fidelity autonomous downhole drilling system that is self-optimizing using real-time drilling parameters and able to precisely predict the optimal rate of penetration is essential. Optimizing the input parameters; surface weight on bit (WOB), and rotary speed (RPM) which in turns improves drilling performance and reduces well delivery cost is not trivial due to the complexity of the non-linear bit-rock interactions and changing formation characteristics. However, application of derived variables shows potential to predict rate of penetration and determine the most influential parameters in a drilling process. In this study the use of derived controllable variables calculated from the drilling inputs parameters were evaluated for potential applicability in predicting penetration rate in autonomous downhole drilling system using the artificial neutral network and compared with predictions of actual input drilling parameters; (WOB, RPM). First, a detailed analysis of actual rock drilling data was performed and applied in understanding the relationship between these derived variables and penetration rate enabling the identification of patterns which predicts the occurrence of phenomena that affects the drilling process. Second, the physical law of conservation of energy using drilling mechanical specific energy (DMSE) defined as energy required to remove a unit volume of rock was applied to measure the efficiency of input energy in the drilling system, in combination with penetration rate per unit revolution and penetration rate per unit weight applied (feed thrust) are used to effective predict optimum penetration rate, enabling an adaptive strategize which optimize drilling rate whilst suppressing stick-slip. The derived controllable variable included mechanical specific energy, depth of cut and feed thrust are calculated from the real- time drilling parameters. Artificial Neutral Networks (ANNs) was used to predict ROP using both input drilling parameters (WOB, RPM) and derived controllable variables (MSE, FET) using same network functionality and model results compared. Results showed that derived controllable variable gave higher prediction accuracy when compared with the model performance assessment criteria commonly used in engineering analysis including the correlation coefficient (R2) and root mean square error (RMSE). The key contribution of this study when compared to the previous researches is that it introduced the concept of derived controllable variables with established relationship with both ROP and stick-slip which has an advantage of optimizing the drilling parameters by predicting optimal penetration rate at reduced stick-slip which is essential in achieving an autonomous drilling system. :
- Europe (0.93)
- North America > United States > Texas (0.28)
- Asia > Middle East > Iran > Khuzestan (0.28)
Abstract Over the years, the prediction of penetration rate (ROP) has played a key rule for drilling engineers due it is effect on the optimization of various parameters that related to substantial cost saving. Many researchers have continually worked to optimize penetration rate. A major issue with most published studies is that there is no simple model currently available to guarantee the ROP prediction. The main objective of this study is to further improve ROP prediction using two predictive methods, multiple regression analysis (MRA) and artificial neural networks (ANNs). A field case in SE Iraq was conducted to predict the ROP from a large number of parameters. A databases from one well drilled in carbonate environments were subjected to the predictive methods. Each raw dataset is described by eight parameters including rate of penetration (ROP), true vertical depth (TVD), weight on bit (WOB), bit rotational speed (RPM), torque (TQ), flow rate (Q), equivalent circulating density (ECD), standpipe pressure (SPP), and unconfined compressive strength (UCS). First, both MRA and ANNs are tested as predictive methods. The prediction capacity of each model was also verified by using two-based error metrics: the determination coefficient (R) and the mean square error (MSE). The current results support the evidence that MRA and ANNs are able to be effectively utilize the drilling data, and thus provide accurate ROP prediction. However, more attention to the multiple regression analysis is required where it is implemented for ROP prediction. ANNs appear to be more conservative in predicting ROP than MRA as indicated by a higher value R (0.96) and lower value MSE (1.89) of the ANN model. Considering the input parameters, the obtained results showed that TVD, WOB, RPM, SPP, and ECD had the greatest effect on estimated ROP-conditions, followed in decreasing by pump flow rate, drilling torque, and rock strength. Another important point that highlights in this study is that the drilling rate may increase with depth in carbonate rocks because of their heterogeneity. This study presents new models to estimate ROP from other parameters which can help the driller to achieve an optimal drilling rate through monitoring controllable parameters.
- Asia > Middle East > Iraq > Basra Governorate (0.28)
- Asia > Middle East > Iran > Khuzestan (0.28)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geology > Rock Type > Sedimentary Rock > Carbonate Rock (0.34)
- Well Drilling > Drilling Operations (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Reservoir geomechanics (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Neural networks (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (1.00)