Utilizing Machine Learning Methods to Estimate Flowing Bottom-Hole Pressure in Unconventional Gas Condensate Tight Sand Fractured Wells in Saudi Arabia (Russian)

2020 ◽  
Author(s):  
Fahad Hassan Al Shehri ◽  
Anton Gryzlov ◽  
Tayyar Al Tayyar ◽  
Muhammad Arsalan
Keyword(s):  
Machine Learning ◽  
Saudi Arabia ◽  
Gas Condensate ◽  
Learning Methods ◽  
Unconventional Gas ◽  
Hole Pressure ◽  
Machine Learning Methods ◽  
Bottom Hole Pressure ◽  
Bottom Hole

Estimation of Bottom Hole Pressure in Electrical Submersible Pump Wells using Machine Learning Technique

2021 ◽  
Author(s):  
Sherif Sanusi ◽  
Adenike Omisore ◽  
Eyituoyo Blankson ◽  
Chinedu Anyanwu ◽  
Obehi Eremiokhale
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Oil And Gas Industry ◽  
Supervised Machine Learning ◽  
Power Cable ◽  
Submersible Pump ◽  
Hole Pressure ◽  
Bottom Hole Pressure ◽  
Bottom Hole ◽  
Electrical Submersible Pump

Abstract With the growing importance and application of Machine Learning in various complex operations in the Oil and Gas Industry, this study focuses on the implementation of data analytics for estimating and/or validating bottom-hole pressure (BHP) of Electrical Submersible Pump (ESP) wells. Depending on the placement of the ESP in the wellbore and fluid gravity of the well fluid, there can be little or no difference between BHP and Pump intake Pressure (PIP); hence these two parameters were used interchangeably. The study focuses majorly on validating PIP when there are concerns with downhole gauge readings. It also has application in estimating PIP when the gauge readings are not available, provided the relevant ESP parameters are obtainable. ESP wells generally have gauges that operate on "Comms-on-Power" principle i.e. downhole communication is via the power cable and loss of signal occurs when there is no good electrical integrity along the electrical path of the ESP system. For proper hydrocarbon accounting and statutory requirements, it is important to have downhole pressure readings on a continuous basis, however this cannot be guaranteed throughout the life cycle of the well. Therefore, an alternative method is essential and had to be sought. In this study, the Response Surface Modelling (RSM) was first used to generate a model relating the ESP parameters acquired real-time to the PIP values. The model was fine-tuned with a Supervised Machine Learning algorithm: Artificial Neural Network (ANN). The performance of the algorithms was then validated using the R-Square and Mean Square Error values. The result proves that Machine Learning can be used to estimate PIP in a well without recourse to incurring additional cost of deploying new downhole gauges for acquisition of well and reservoir data.

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.

Prediction Of Shut-in Bottom-hole Pressure From Wellhead Pressure Considering Transient Behaviour Of Hydrostatic Pressure Loss During Shut-in

2021 ◽  
Author(s):  
Satoshi Maki
Keyword(s):  
Hydrostatic Pressure ◽  
Pressure Loss ◽  
Gas Condensate ◽  
Gas Field ◽  
Wellhead Pressure ◽  
Transient Behaviour ◽  
Hole Pressure ◽  
Bottom Hole Pressure ◽  
Bottom Hole ◽  
Surface Data

Abstract Shut-in bottom-hole pressure (SIBHP) is key information to monitor reservoir depletion and thus to evaluate gas in place for a gas field. Permanent downhole gauges are widely used to continuously measure bottom-hole pressure especially for subsea wells. However, when gauges fail, a large cost of workover for gauge replacement is a major problem. In case of the gauge failure, SIBHP hence needs to be estimated from measured surface data such as wellhead pressure (WHP) and wellhead temperature (WHT). The use of WHT for subsea wells however leads to a large error of the SIBHP calculation because sea current significantly affects WHT readings during shut-in. This study aims to develop a correlation methodology to predict SIBHP from surface data without using WHT. We have developed a linear correlation of hydrostatic pressure loss during shut-in with superposition time to predict SIBHP from WHP. Using superposition time of well production status with zero or one indicator effectively accounts for transient behaviour of hydrostatic pressure loss caused by cooling of wellbore fluid and pressure build-up. The transient behaviour differs by individual wells. Hence, the coefficients of the correlation were calibrated well by well to reproduce observed SIBHP. The correlations were applied to the Ichthys gas-condensate field located in the Browse Basin, North West Shelf of Australia. SIBHP trends are reasonably reproduced by the correlations after calibration. A blind test was performed using additional 18 months production data to investigate the predictability of the proposed correlation. As a result, high accuracy of the prediction is confirmed with an average absolute error of approximately 0.4% for the test period. The SIBHP of wells in which downhole gauges have failed is predicted by the calibrated correlations, and the predicted SIBHP is utilised for the reservoir management of the Ichthys field. We present the novel methodology to predict SIBHP from observed surface data without WHT for subsea gas-condensate wells considering the transient behaviour of hydrostatic pressure loss. The proposed correlation provides accurate prediction of SIBHP in case of the gauge failure.

Optimising Well Design in Saudi Arabia: Successful Application of Managed Pressure Drilling Enables Drilling Across Multiple Pressure Zones and Running Liner Using Constant Bottom Hole Pressure Technique

2014 ◽  
Author(s):  
Nelson O Pinero Zambrano ◽  
Ibraheem M Al-Ageel ◽  
Muhammad Abdul Muqeem ◽  
Abdulaziz Sallim Al Mutawa ◽  
Mohamed Cherif Mazouz ◽  
...  
Keyword(s):  
Saudi Arabia ◽  
Hole Pressure ◽  
Bottom Hole Pressure ◽  
Bottom Hole ◽  
Well Design ◽  
Managed Pressure Drilling
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