Integrated Hydraulics Modeling for Managed Pressure Drilling

As petroleum exploration and development extend towards marginal, deep, and unconventional resources, drilling challenges such as narrow density windows and high wellbore control risks become increasingly prominent. Managed pressure drilling (MPD) proves effective in reducing drilling operational difficulties and well control risks in narrow safe-density window formations. However, existing MPD systems have limited capabilities for real-time calculation and analysis in the absence of downhole pressure measurement data, hindering accurate guidance for real-time calculation, analysis, and control decision recommendations in both conventional and precise MPD operations.Therefore, based on on-site engineering logging, real-time data from MPD operations, a branch decision tree was established to automatically determine the operational conditions of MPD. This achieved real-time identification of 11 drilling conditions. Starting from the characteristics of two-phase fluid flow in the annulus, coupling wellbore pressure and formation fluid invasion, a predictive model for annular pressure distribution during MPD was established. This model facilitated the calculation, determination, and recommendation of the pre-control value for wellhead backpressure. Building upon theoretical research, a MPD calculation, analysis, and control decision system was developed. Field testing and validation indicated that the system could simulate and analyze wellbore pressure and wellhead backpressure values, automatically identify drilling conditions and downhole complexities. The average error between the calculated and measured values of the standpipe pressure was 1.88%. The accuracy of the predicted wellhead backpressure values based on safe operating conditions compared to the actual controlled values reached 89.8%. This system achieved real-time calculation, analysis, and control decision-making based on real-time engineering parameters, enhancing the reliability and timeliness of wellhead backpressure control in MPD operations. It effectively guided conventional MPD operations and improved the capabilities and intelligence level of precision MPD operations after process optimization.

show abstract

Identification of Potential Candidate's Wells for Workover

Okon

Appah

2021

Day 2 Tue, August 03, 2021

View full text Add to dashboard Cite

To maximize production from mature fields, it is essential to identify candidate's wells that are not producing up to their potential. Performing periodic interventions or workovers in wells is an established approach for arresting production decline and maximizing production from the fields. However, for mature fields with large well counts, the process of determining the best candidates for well interventions can be complicated and tedious. This can result in less-than-optimal outcomes. Advanced data analytics modeling offers quick and easy access to important information. The main objective of this study is to identify potential candidate wells for workover operation ahead of time so that we can fix them before they become problem. This was achieved via principal component analysis with the aid of XLSTAT in Excel. In this study, we developed a model based on PCA to quickly identify and rank the workover candidate's wells. The dataset used in this project comprises of 66 oil wells and were obtained from a field operating in the Niger Delta. The first step involved data gathering and validation and uploading into XLSTAT software. Data preprocessing procedures were conducted to condition the dataset so as to give optimum performance during model development. A model was built to identify potential wells for workover operation. The results obtained here showed that the wells are separated to areas designated as (A to E). Wells found in area A indicated that they are potential candidates for workover operation. Wells found in area B showed that they are not under immediate danger, but attention would be needed to monitor and prevent increasing water and gas rates in the future. Wells found in area C indicated that they required immediate attention to prevent further decline in oil production. Likewise, wells found in Area D indicated that they also required immediate attention to prevent further decline in oil production. Finally, Wells found in Area E showed that they have highest oil production, lowest water production and moderate gas production, indicating normal condition with no immediate workover operation required. With advanced data analytics modeling, reservoir engineers or geoscientists will now see a bigger picture either field by field or reservoir by reservoir and quicky identify potential candidate wells for workover operation ahead of time before they become a problem. Hence, the results of the analysis can help us to better target wells that are potential candidates for high water cut, high WOR, High gas rates and low oil rates.

show abstract

Integrated Hydraulics Modeling for Managed Pressure Drilling

Cited by 4 publications

References 5 publications

Kick detection and remedial action in managed pressure drilling: a review

Kick detection and remedial action in managed pressure drilling: a review

Decision-making method of managed pressure drilling based on real-time calculation and analysis models

Identification of Potential Candidate's Wells for Workover

Contact Info

Product

Resources

About