PETROPHYSICAL SURVEILLANCE - THE KEY DRIVER IN OPTIMISING WELLS PERFORMANCE
Speaker: Maryam Mousavi (BP AMERICA)
Date: Wednesday, Oct 3rd, 2018
Venue: Weatherford Lab: 5200 North Sam Houston Parkway West Suite 500, Houston, 77086
Time: 11:30 am – 01:00 pm
Admission: Regular Admission ($15). Student and In Transition Professionals ($10)
Parking Info: Visitors are requested to reverse park, note their license plate number, and sign in at the main reception.
Please register by Oct 1st 2018 @ 12 pm to reserve lunch and pre-registration
Contact: Fransiska Goenawan
Corresponding: email@example.com // 281-460-8692
Reservoir quality and completion efficiency are key factors that impact well performance. The relationship becomes more complex for the case of Cased Hole Gravel-Pack and Frac-Pack completions. Most of the time, completion quality and reservoir description were treated in isolation, by different disciplines deploying dissimilar surveillance methods. This can negatively impact the decision making and the cost of data acquisition. Evolution of Multi-Detector Pulsed Neutron (MDPN) technology and the improvement of nuclear attributes extraction (spectral processing) enables us to simplify the data acquisition program while expanding the applications to both reservoir and completion evaluation.
Our paper will describe two case studies from the Gulf of Mexico where deployment of Multi-Detector Pulsed Neutron technology saved significant rig time and reduced the production deferment.
In this case study, various nuclear attributes were used to extract information about connate water distribution, something that has been observed to be variable in the field. This will reduce the uncertainty in MDPN derived Saturation and impact further the reservoir model when reconciled with other data e.g. Production log (PL).
Data acquired with the same MDPN instrumentation has been used to evaluate the quality of the ceramic proppant packing these wells. This approach introduces rigor in acquisition, processingand evaluation, offering a robust answer over traditional methods such as density based and radioactive tracer logs.
Maryam a. Mousavi is apetrophysicist in BP. She has a PhD in Petroleum Engineering from University of Texas at Austin and BSc and MSc in Geology from University of Tehran, Iran. She works as a petrophysicist for Mad Dog asset in BP, planning, execution and interpretation of open hole and cased hole logs. She has worked on variety of reservoirs including siliciclastics and carbonates. She also has done extensive research on pore scale modeling of two phase flow in tight gas sandstones as her PhD thesis.
Maryam Worked in Bureau of Economic Geology fromUniversity of Texas at Austin as a Post-Doctoral fellow working on sequence stratigraphic-forward modeling of growth-faulted sub-basins of the Oligocene Frio Formation. On her Master’s degree, she worked on porosity and permeability estimation using Neural Network from Dalan formation in South Pars field in Persian Gulf. Maryam has several papers and presentations in different Oil and Gas industry journals and symposiums. She is a member of SPE and SPWLA
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