Rojas, Pedro A. Romero (Weatherford International) | Cristea, Alexandrina (Weatherford International) | Pavlakos, Paul (Weatherford International) | Ergündüz, Okan (ARAR AS) | Kececioglu, Tayfun (ARAR AS) | Alpay, Server Fatih (ARAR AS)
Nuclear magnetic resonance wireline logging and data post-processing technologies are continuously evolving, making significant contributions to rock, fluid typing, formation evaluation and characterization of the near-wellbore zone. In heavy oil fields, however, nuclear magnetic resonance (NMR) logging is known to provide an underestimated permeability, poor reliable oil typing and thus poor oil saturation and viscosity determinations, especially when the evaluation is based only on the spectra of transverse magnetic relaxation times (T2) (one-dimension NMR) [Romero et al., 2009]. Several attempts have been made to improve NMR results, mostly with limited success [Fang et al., 2004], especially in separating the oil component from the contribution of other fluids to the T2 spectra. The main reason lies not necessarily in the selection of the data acquisition parameters and sequences for a single-frequency or multi-frequency tool, but in the way how the data is post-processed.
The present study refers to a well drilled through the Derdere formation, a limestone/dolomite heavy oil reservoir in Turkey. The NMR data was acquired in with a centralized, single-frequency wireline tool in a 6-in. borehole, drilled with water-based mud in a freshwater carbonate reservoir. The generated T2 log was analyzed in a traditional way to obtain the NMR total porosity and its partitions based on standard cutoff values. For the given 12 API oil gravity, reservoir temperature (76 °C) and gas-oil-ratio (GOR) the T2Oil peak appears around 170 ms, right from the T2 cutoff for limestones; therefore, no corrections were needed on the permeability calculated from the Timur-Coates and Schlumberger-Doll-Research (SDR) equations. In the present well, only a diffused separation between oil and free water could be observed on the T2 distribution log from field data.
In the broader concept of Artificial Intelligence, the newly proposed post-processing steps to obtain the oil saturation start by deconvolving the T2 spectra, using blind source separation (BSS) based on independent component analysis (ICA) [Romero, 2016; Romero Rojas et al., 2018]. Based on its T2 peak value —the expected T2Oil peak response— calculated from the prejob planner/simulator, the deconvolution results show that one specific independent component corresponds to the oil, from which the oil saturation was determined.
Results demonstrated the usefulness of NMR logging technology in the characterization and evaluation of this reservoir. Data post-processing based on BBS-ICA enable adequate differentiation between fluid components from T2 spectra. For the reasons above, NMR has been proposed for additional wells in the same field.
Africa (Sub-Sahara) Marathon Oil has produced first gas from its Alba B3 compression platform offshore Equatorial Guinea. The startup enables the company to convert approximately 130 million BOE of proved undeveloped reserves, which more than doubles its remaining proved developed reserve base in the country. Marathon holds an operating interest of about 65% in the field, with Noble Energy holding the remaining stake. Aminex said that gas production from the Kiliwani North-1 well in Tanzania has reached 30 MMcf/D (about 5,000 BOE/D). The project's commissioning process is expected to conclude with a well test to determine the optimal production rate, which previous test data suggest will be approximately 30 MMcf/D, the company said. The operator of the Kiliwani North Development License, Aminex holds a 54.575% interest in the well.
Genel Energy said that one of its wells drilled to appraise the northern end of the Taq Taq oil field in the Kurdistan region of Iraq encountered oil-bearing reservoirs. The main objective of the well, which had been drilled since February, was to reduce uncertainty about the free water level in the field's north flank. Further development of the Cretaceous reservoir had been deferred pending the test program results. The company said the well, which was drilled to a measured depth of 10,171 ft, encountered good quality Cretaceous Shiranish and Kometan reservoirs but that it was too early to estimate the impact of the results on reserves, production, or future investment. The field now produces a gross 15,100 B/D of oil.
When drilling in an arid region through heavily fractured formations, it can be very challenging to manage drilling-fluid losses and at the same time maintain a downhole-pressure gradient that is compatible with the very-low geopressure gradient windows that are typically encountered in those drilling conditions. Nitrogen- enriched drilling muds may provide a good solution to both problems; however, the properties - such as density, rheology, specific-heat capacity, and thermal conductivity - of this type of drilling fluid are highly dependent on temperature and pressure, and in most cases those characteristics cannot be measured in situ, making it difficult to estimate the actual downhole-pressure conditions. The approach described in this paper consists of the reconstruction of the drilling-fluid-mix properties from the characteristics of its components and the incorporation of the resulting pressure- and temperature-dependent constitutive laws into a realtime multiphase- and multicomponent-drilling hydraulic model to estimate the downhole pressures along the drillstring and borehole as a function of the drilling parameters. Because of the uncertainty of some of the characteristics of the components of the drilling fluid as well as their actual proportion in the mix, the modeled values are only valid within a certain accuracy. Stochastic simulations are made during the estimation of the downhole pressures to ascertain the precision of the calculations. As a consequence, by comparing the obtained interval o confidence on the estimations with actual measurements, it is possible to evaluate whether the drilling conditions are normal or deteriorating. The validity and performance of the derived fluid-model extension are tested by use of a real-time data set recorded during the drilling of a well in the Erbril area of the Kurdish region of Iraq, by use of the wellsite information transfer standard markup language drilling-data-exchange protocol. The model results are reviewed and compared with the actual measurements recorded during the drilling operations. The potential sources of limitation, discrepancy, or error between the modeled and observed well and fluid behavior are discussed, along with potential explanations for the observed wellbore physics seen in the recorded-data feed.
This case study involves a well drilled in the Erbil region of Kurdistan, a region characterized by a low water table and challenging geological conditions for drilling. To achieve the key drilling objectives, the drilling mud was made more buoyant by the addition of nitrogen into the mud column.
In order to get a full understanding of the downhole conditions using this mud, the complete drilling process was modelled in real-time. The model was driven using a real-time WITSML data feed. This transient modeling software calculates downhole pressures, temperatures, torque and drag and cuttings density at all depths in the well bore in real-time, including the depths where there are no physical measurements.
The transient model is continuously updated in real-time to reflect the drilling processes undertaken on the rig (e.g. pipe movement, mud pump activity, thermodynamics). Surface system variables including virtual mud pit levels are also calculated in real-time.
The modelled data is then continuously compared to the sparse data points that are being recorded in real-time, allowing both a continuous calibration of the model with the “as drilled” well operation. The calculation of important drilling parameters such as sliding friction, rotational friction, and hydraulic friction is performed in real-time.
The paper will present the key observations upon the matches between the modelled data and the "as drilled" data and summarise the key lessons learned during the well operations and the real-time modelling processes.