scholarly journals Sensitivity Analysis of the Parameters Affecting Bottom-hole Pressure during Foam Underbalanced Drilling

2021 ◽  
Vol 804 (2) ◽  
pp. 022023
Author(s):  
Xiaoning Zhang ◽  
Lingdong Li ◽  
Jianli Zhang ◽  
Luyi Duan ◽  
Guangtai Wang
Keyword(s):  
Sensitivity Analysis ◽  
Hole Pressure ◽  
Bottom Hole Pressure ◽  
Underbalanced Drilling ◽  
Bottom Hole

A Machine Learning Method to Infer Inter-Well Connectivity Using Bottom-Hole Pressure Data

2020 ◽  
Vol 142 (10) ◽  
Author(s):  
Wei Liu ◽  
Wei David Liu ◽  
Jianwei Gu
Keyword(s):  
Machine Learning ◽  
Sensitivity Analysis ◽  
Flow Direction ◽  
Time Lag ◽  
Time Lags ◽  
Pressure Data ◽  
Actual Case ◽  
Hole Pressure ◽  
Bottom Hole Pressure ◽  
Bottom Hole

Abstract In the production and development of oil fields, production wells generally produce at a constant rate since the fixed production is easier to control than the fixed pressure. Thus, it is more feasible to use bottom-hole pressure data for connectivity analysis than historical injection and production data when producers are set in fixed rates. In this work, a practical procedure is proposed to infer inter-well connectivity based on the bottom-hole pressure data of injectors and producers. The procedure first preprocesses the bottom-hole pressure based on nonlinear diffusion filters to constitute the dataset for machine learning. An artificial neural network (ANN) is then generated and trained to simulate the connection relationship between the producer and its adjacent injectors. The genetic algorithm (GA) is also introduced to avoid the tedious process of determining time lags and other hyper-parameters of ANN. In particular, the time lag is normally determined by subjective judgment, which is optimized by GA for the first time. After optimizing the parameters, the sensitivity analysis is performed on the well-trained ANN to quantify inter-well connectivity. For the evaluation and verification purposes, the proposed GA and sensitivity analysis based ANN were applied to two synthetic reservoirs and one actual case from JD oilfields, China. The results show that the calculated connectivity conforms to known geological characteristics and tracer test results. And it demonstrates that the presented approach is an effective alternative way to characterize the reservoir connectivity and determine the flow direction of injected water.

Managed Pressure Drilling and Underbalanced Drilling Compounded Role in Solving the Exploration and Appraisal Challenges of Biogenetic Gas Resources in the United Arab Emirates -Case Study

2021 ◽  
Author(s):  
Ahmed Al Mutawa ◽  
Ibrahim Hamdy ◽  
Eias Daban Al Shamisi ◽  
Bassem El Yossef ◽  
Mohamed Sameer Amin ◽  
...  
Keyword(s):  
United Arab Emirates ◽  
Drilling Fluid ◽  
Productivity Index ◽  
Biogenic Gas ◽  
Hole Pressure ◽  
Bottom Hole Pressure ◽  
Underbalanced Drilling ◽  
Bottom Hole ◽  
Novel Approach ◽  
Managed Pressure Drilling

Abstract Biogenic gas resources have gathered importance recently due to its widespread availability, occurrence at geologically predictable circumstances, and existence at shallow depths. It is estimated that biogenic gas forms more than 20% of the global discovered reserves. However, the exploration and development of these unconventional resources come with numerous drilling and reservoir challenges. This paper showcases a novel approach used in the United Arab Emirates to overcome these challenges using managed pressure and underbalanced drilling. To tackle both reservoir and drilling challenges, a hybrid solution combining Underbalanced (UBD) and Managed Pressure Drilling (MPD) was applied. UBD was used to characterize the reservoir in terms of pressure and productivity index to ultimately enhance productivity by eliminating formation damage. MPD was used next to continue drilling through the problematic zone which had high instability due to the presence of highly sensitive salt, in addition to the presence of high pressure and loss zones. The fit for purpose hybrid application design allowed the operator to immediately switch between UBD and MPD conditions, as the well required with the same equipment. Three of the four targeted formations were in the 8 ½″ hole section, UBD was selected to drill the first reservoir formation which allowed pore pressure verification and avoided using excessive mud weight that was the culprit of many challenges like slow ROP, drilling fluid losses, bit balling, and fracking the formations. UBD has proved that mud weight can be reduced by 20%-30% comparing to conventional drilling. The second formation was a salt formation that has caused previously hole collapse and losses-kicks problems as heavy mud used to drill this salty formation. MPD used successfully drill this section by constant bottom hole pressure and lower mud weight as it was found from analyzing offset wells reports that hole collapse occurred at connections and pump off events. Constant Bottom Hole Pressure (CBHP) also eliminated tight spots and excessive reaming resulting in optimized drilling. The third formation used MPD as well to minimize overbalance pressure over previous sections while the fourth formation was drilled by UBD as it had a separate 6″ hole section as it formed an independent reservoir. The combined MPD and UBD approach eliminated most the NPT encountered in offset wells, enhanced Rate of Penetration (ROP) by 200% to 300% and slashed the well drilling time by 27 days.

scholarly journals Production behavior evaluation on multilayer commingled stress-sensitive carbonate gas reservoir

2020 ◽  
pp. 014459872096415
Author(s):  
Jianlin Guo ◽  
Fankun Meng ◽  
Ailin Jia ◽  
Shuo Dong ◽  
Haijun Yan ◽  
...  
Keyword(s):  
Early Stage ◽  
Sedimentary Environment ◽  
Production Performance ◽  
Total Production ◽  
Inversion Algorithm ◽  
Gas Reservoir ◽  
Hole Pressure ◽  
Bottom Hole Pressure ◽  
Bottom Hole ◽  
Production Behavior

Influenced by the complex sedimentary environment, a well always penetrates multiple layers with different properties, which leads to the difficulty of analyzing the production behavior for each layer. Therefore, in this paper, a semi-analytical model to evaluate the production performance of each layer in a stress-sensitive multilayer carbonated gas reservoir is proposed. The flow of fluids in layers composed of matrix, fractures, and vugs can be described by triple-porosity/single permeability model, and the other layers could be characterized by single porosity media. The stress-sensitive exponents for different layers are determined by laboratory experiments and curve fitting, which are considered in pseudo-pressure and pseudo-time factor. Laplace transformation, Duhamel convolution, Stehfest inversion algorithm are used to solve the proposed model. Through the comparison with the classical solution, and the matching with real bottom-hole pressure data, the accuracy of the presented model is verified. A synthetic case which has two layers, where the first one is tight and the second one is full of fractures and vugs, is utilized to study the effects of stress-sensitive exponents, skin factors, formation radius and permeability for these two layers on production performance. The results demonstrate that the initial well production is mainly derived from high permeable layer, which causes that with the rise of formation permeability and radius, and the decrease of stress-sensitive exponents and skin factors, in the early stage, the bottom-hole pressure and the second layer production rate will increase. While the first layer contributes a lot to the total production in the later period, the well bottom-hole pressure is more influenced by the variation of formation and well condition parameters at the later stage. Compared with the second layer, the scales of formation permeability and skin factor for first layer have significant impacts on production behaviors.

scholarly journals A Unified Formula for Determination of Wellhead Pressure and Bottom-hole Pressure

2013 ◽  
Vol 37 ◽  
pp. 3291-3298 ◽  
Author(s):  
Mingze Liu ◽  
Bing Bai ◽  
Xiaochun Li
Keyword(s):  
Wellhead Pressure ◽  
Hole Pressure ◽  
Bottom Hole Pressure ◽  
Bottom Hole
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