International Journal of Applied Engineering Research ISSN 0973-4562 Volume 12, Number 22 (2017) pp. 12880-12891 © Research India Publications. http://www.ripublication.com 12880 Modeling of Drilling Rate of Penetration Using Adaptive Neuro-Fuzzy Inference System Mohammed Ayoub 1* , Goh Shien 1 , Diab Diab 1* and Quosay Ahmed 2 1 Petroleum Engineering Department, Universiti Teknologi, Petronas Bandar Seri Iskandar, 32160 Perak, Malaysia. 2 Department of Oil & Natural Gas Engineering, University of Khartoum, 11111 Khartoum, Sudan. (*Correspondence Author) 1 Orcid: 0000-0002-5007-779X Abstract Drilling rate of penetration (ROP) is a crucial factor in optimizing drilling cost. This is mainly due to the excessive cost of the drilling equipment and rig rental, where the longer the drilling activity would reflect a higher expenditure. If the drilling rate of penetration can be predicted accurately, we would be able to avoid unnecessary spending. Hence, this can lead to minimizing the drilling cost significantly. In this paper, an Adaptive Neuro-Fuzzy Inference System (ANFIS) model is generated using MATLAB environment. A total number of 504 data sets from a Sudanese oilfield is used to develop a well- trained and tested ANFIS model for ROP prediction. The parameters included in the model generation are: depth, bit size, mud weight, rotary speed and weight on bit. Training options were set to give the best predicted ROP against the real data. This model is proven to give a high performance with an error as low as 1.47% and correlation coefficient of 98%. With this model, the estimation of the duration of drilling activities in the nearby wells can be done accurately if relevant data from the same reservoir is available. Caution must be taken to avoid using the results from this model beyond the range of training data. Keywords: rate of penetration; neuro-fuzzy; bit size; mud weight; rotary speed; weight on bit. INTRODUCTION Drilling optimization is the key aspect to achieve minimum cost and making the drilling operation economically feasible. By understanding the drilling parameters that affect drilling rate of penetration (ROP), a model can be constructed to predict the drilling rate of penetration through a formation. The ability to predict ROP accurately will result in avoiding unnecessary spending and hence cut the drilling budget considerably. For this reason, several authors and researchers attempted to develop a model for ROP prediction. Generally, the drilling rate of penetration is a dependent parameter that can be predicted as a function of independent drilling parameters [1]. The drilling parameters are divided into three categories: formation related, drilling bit related and hydraulics down hole. There are some models proposed in the past in the effort of predicting ROP including Bingham model, Bourgoyne and Young model, Warren model etc. although these models are less accurate in predicting ROP, they are always used as a guideline for modification of mathematical models in these days. In This paper, a Neuro-Fuzzy model is generated using ANFIS architecture in MATLAB. A set of data consists of 504 Data samples from Sudanese oil field was used to train and test the model. Then, trend analysis is curried out for the Neuro-Fuzzy, Bingham mode and Bourgoyne and Young model (BYM). From the results, the Neuro-Fuzzy model shows a high performance in predicting ROP against the real data. The model can be used to predict the ROP of the nearby well in the same reservoir. However, one limitation of Neuro-Fuzzy technique is that it doesn’t generate a universal model that can be used to predict ROP for all the fields all over the world. Instead, the model must be retrained if it is to be used in another reservoir. It’s also recommended to train the model using a wide range of data to enhance the model reliability. FACTORS AFFECTING THE DRILLING RATE OF PENETRATION (ROP) Prediction of ROP is a complicated task because it involves many factors [2, 3]. Researchers realized that ROP is a function of rock properties such as: the uniaxial compressive and tensile