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ABSTRACT The Rumaila field is in South East Iraq and contains multiple reservoir intervals, including the Upper Cretaceous Mishrif carbonate reservoir, one of the major reservoirs in the world, that has been producing for more than 50 years. One of the key challenges in the Mishrif is to characterize the pore structure distinction between primary and secondary porosity. The secondary porosity in the form of large pores, if present, dominates the petrophysical properties, especially permeability. Advanced logs e.g. Nuclear magnetic resonance (NMR) and image logs can be utilized to understand the variations in pore structure both qualitatively and quantitatively. In this paper we focused primarily on four new wells with very comprehensive logging and coring programs. NMR logs were acquired using different tools and pulse sequences. This resulted in uncertainty in porosity and T2 distributions and consequently complications in the NMR interpretation. We observed two key issues: porosity deficit due to lack of polarization and T2 distribution truncation due to the low number of echoes. We used a single pore model to reproduce the NMR response in different pore sizes and fluid types for different pulse sequences. The results showed that the NMR response, especially in water (water-based mud filtrate) filled large pores, is sensitive to polarization time, echo spacing and tool gradient strength. NMR log data confirmed the modelling results. We recommended an optimum pulse sequence and tool characteristics to fully capture the heterogeneous rock and fluid system in this carbonate reservoir. NMR logs, when available, were the primary tools to identify the large pores. We present a consistent workflow for NMR log analysis that was developed to identify and quantify large pores and extended to all wells in the field. We used advanced NMR interpretation techniques e.g. Factor Analysis (NMR FA, Jain et al, 2013) in a series of oil wells drilled with water-based mud. Using Factor Analysis, we identified a cut off value of 847 ms for large pore volumes. In this manuscript we also present an integration of laboratory measurements e.g. NMR, mercury intrusion capillary pressure data, whole core CT scanning and thin section analysis in our interpretation workflow. We also compared the large pore volume from image logs with NMR logs and other laboratory data and observed very consistent results. All the available information was integrated to build an "NMR-based" petrophysical model for porosity, rock type, permeability and saturation determination. The NMR-based model was very comparable with the classic FZI rock typing. The results of this study were used to modify the NMR acquisition program in the field and to build a petrophysical model based on only NMR and image log measurements for carbonate reservoirs. In this paper, we will discuss NMR modelling and corresponding log data from various wells to confirm the results. Furthermore, we will present novel interpretation workflow integrating laboratory measurements and log data which led to the modification of the NMR acquisition program in the field and creation of a data-driven petrophysical model based on only NMR and image log measurements for carbonate reservoirs.
Kozlowski, Maciej (Halliburton) | Chakraborty, Diptaroop (Halliburton) | Jambunathan, Venkat (Halliburton) | Lowrey, Peyton (Halliburton) | Balliet, Ron (Halliburton) | Engelman, Bob (Halliburton) | Ånensen, Katrine Ropstad (Aker BP) | Kotwicki, Artur (Aker BP) | Johansen, Yngve Bolstad (Aker BP)
The Alvheim Field in the Norwegian North Sea was discovered in 1998. Two wells were drilled in 2018 in the Gekko structure to confirm oil column height and to evaluate reservoir quality in the Heimdal Formation. A comprehensive wireline logging program, including NMR and formation testing, was optimized to reduce formation evaluation uncertainty. Evaluating fluid properties, oil column height, and reservoir quality were primary objectives. Well A was first drilled on the south of the structure, followed by Well B on the north of the structure. Reservoir quality encountered in both wells was very good, and a project to develop these resources is currently in the selection phase. Formation evaluation uncertainty encompassing pore geometry distribution, permeability, reservoir quality, and hydrocarbon identification are mitigated by studying the nuclear magnetic resonance (NMR) log response. NMR fluid typing has been widely used in the oil industry since the 1990s. NMR fluid typing today is a combination of the contrast of spin relaxation time T1, the spin-spin relaxation time T2 (T1T2), and the diffusivity (T2D) of formation fluids (Chen et al., 2016). NMR fluid typing can be obtained from a continuous log and/or stationary log measurements. This paper showcases excellent, textbook-quality NMR data, as well as the integration of NMR data in the petrophysical workflow. High-confidence fluid properties and fluid contacts are determined. This paper also highlights a comparison of NMR data acquired in stationary vs. continuous depth-based log modes in both wells. The continuous log data quality is equivalent to stationary data, implying continuous log data quality is sufficient for reliable NMR fluid properties evaluation without depending on time-consuming stationary NMR measurements. Reducing logging operations rig time is very advantageous in the North Sea, where drilling rig operations cost is high, and enhanced rig time management is constantly required.
The Rumaila field is in South East Iraq contains multiple reservoir intervals, including the Upper Cretaceous Mishrif carbonate reservoir, one of the major reservoirs in the world, that has been producing at considerable oil rates for more than 50 years. With billions of barrels yet to be recovered it is expected to play a significant role in sustaining Rumaila production for decades. Reservoir pressure has dropped due to historical production and, therefore, large scale water injection is planned to support and enhance future production rates and oil recovery.
One of the key subsurface challenges in carbonate reservoirs is to understand and characterise reservoir complexity and heterogeneity, with permeability being one of the key factors in understanding sweep behavior and predicting production and injection rates. Rumaila has extensive surveillance programs and production and saturation logs in particular are used to refine static and dynamic models and to better characterise individual well performance. With more than 1,000 well penetrations to date, efficient management of wells is key to optimising production.
It was recognized several years ago that the available log and core datasets at that time did not enable a fully characterised model of the pore system, resulting in a large uncertainty in the permeability model. As a result, four new wells were cored, and advanced modern logs acquired to expand the datasets to support a rebuilding of rock typing and permeability models to better understand pore system distributions and the extent and impact of heterogeneity in the Mishrif reservoir.
This paper presents a workflow that utilises NMR logs, NMR core analysis and FZI techniques to predict permeability. The approach is focussed on distinguishing between different pore types by estimating the relative proportion of large pores (Large
Pores Index - LPI) from NMR data and using this as an input to enhance the prediction of FZI rock types and subsequently the prediction of permeability. The results show a significant improvement in permeability estimates compared to more traditional approaches.
The improvement in permeability prediction has been reflected in better predictions of production and injection indexes, improved understanding of sweep behaviour and the prediction of timing for water breakthrough, leading to more optimal management of reservoir performance. Moreover, at the well level, the new model has resulted in enhanced completion decisions for newly drilled wells, as well as ongoing well-work operations (additional perforation and re-perforation campaigns) on existing producers and injectors.
Kausik, Ravinath (Schlumberger-Doll Research) | Prado, Augustin (Schlumberger-Doll Research) | Gkortsas, Vasileios-Marios (Schlumberger-Doll Research) | Venkataramanan, Lalitha (Schlumberger-Doll Research) | Datir, Harish (Schlumberger) | Johansen, Yngve Bolstad (AkerBP)
The computation of permeability is vital for reservoir characterization because it is a key parameter in the reservoir models used for estimating and optimizing hydrocarbon production. Permeability is routinely predicted as a correlation from near-wellbore formation properties measured through wireline logs. Several such correlations, namely Schlumberger-Doll Research (SDR) permeability and Timur-Coates permeability models using nuclear magnetic resonance (NMR) measurements, K-lambda using mineralogy, and other variants, have often been used, with moderate success. In addition to permeability, the determination of the uncertainties, both epistemic (model) and aleatoric (data), are important for interpreting variations in the predictions of the reservoir models. In this paper, we demonstrate a novel dual deep neural network framework encompassing a Bayesian neural network (BNN) and an artificial neural network (ANN) for determining accurate permeability values along with associated uncertainties. Deep-learning techniques have been shown to be effective for regression problems but quantifying the uncertainty of their predictions and separating them into the epistemic and aleatoric fractions is still considered challenging. This is especially vital for petrophysical answer products because these algorithms need the ability to flag data from new geological formations that the model was not trained on as “out of distribution” and assign them higher uncertainty. Additionally, the model outputs need sensitivity to heteroscedastic aleatoric noise in the feature space arising due to tool and geological origins. Reducing these uncertainties is key to designing intelligent logging tools and applications, such as automated log interpretation. In this paper, we train a BNN with NMR and mineralogy data to determine permeability with associated epistemic uncertainty, obtained by determining the posterior weight distributions of the network by using variational inference. This provides us the ability to differentiate in- and out-of-distribution predictions, thereby identifying the suitability of the trained models for application in new geological formations. The errors in the prediction of the BNN are fed into a second ANN trained to correlate the predicted uncertainty to the error of the first BNN. Both networks are trained simultaneously and therefore optimized together to estimate permeability and associated uncertainty. The machine-learning permeability model is trained on a “ground-truth” core database and demonstrates considerable improvement over traditional SDR and Timur-Coates permeability models on wells from the Ivar Aasen Field. We also demonstrate the value of information (VOI) of different logging measurements by replacing the logs with their median values from nearby wells and studying the increase in the mean square errors.
Venkataramanan, Lalitha (Schlumberger Doll Research) | Gruber, Fred K. (GNS Healthcare) | LaVigne, Jack (Schlumberger Doll Research) | Habashy, Tarek M. (Schlumberger Doll Research) | Iglesias, Jorge G. (Schlumberger Doll Research) | Cohorn, Patrick (Bold Energy III LLC) | Anand, Vivek (Schlumberger Doll Research) | Rampurawala, Mansoor A. (Schlumberger Doll Research) | Jain, Vikas (Schlumberger Doll Research) | Heaton, Nick (Schlumberger Doll Research) | Akkurt, Ridvan (Schlumberger Doll Research) | Rylander, Erik (Schlumberger Doll Research) | Lewis, Rick (Schlumberger Doll Research)
In conventional oilfield applications of low-field nuclear magnetic resonance (NMR), data acquisition and analysis are optimal for T2 relaxation in the center of the spectrum, nominally between several milliseconds and several seconds. However, there are numerous applications where the measured magnetization data have short relaxation components, approaching or even below the time resolution of the downhole and/or laboratory measurement. Examples of these applications include heavy oil, organic– shale reservoirs and hydrocarbon and water in small pores. In these applications, the relaxation spectra of interest are typically a few milliseconds. Because the traditional algorithms used to analyze NMR data to estimate porosity and other petrophysical properties involving short relaxation times can be inaccurate, a new algorithm is proposed to improve the accuracy of these parameters. First, a T2 distribution is estimated from the measured magnetization data using traditional inverse–Laplace–transform (ILT) methods. Second, a porosity sensitivity curve is computed for a given pulse sequence and a set of acquisition and inversion parameters. Third, a correction factor is derived from this sensitivity curve and applied seamlessly as part of the inversion so that the overall porosity sensitivity is more uniform at short relaxation times to obtain a modified T2 distribution.The efficacy of the algorithm is illustrated by Monte Carlo simulations and application on two field examples from unconventional shale reservoirs. Prediction of porosity from NMR measurements is particularly useful in unconventional reservoirs for two reasons. First, NMR measurements provide a direct estimate of effective porosity without requiring detailed knowledge of the complex mineralogy typical of shale formations. Second, the deficit between effective porosity predicted from NMR, and total porosity predicted from nuclear logs can be used to obtain accurate estimates of petrophysical quantities, such as, the kerogen content in shales and the hydrogen index in heavy-oil formations. The two field examples are from reservoirs in the Wolfberry trend in the southwestern United States. Application of the new algorithm to NMR data in the first field example results in an increase of up to 10% in porosity in zones with T2 <10 msec. The porosity predictions from the new algorithm show improved correlation with core measurements. In the second field example, the deficit between total porosity and effective porosity predicted from NMR T2 distributions using the new algorithm provides a more accurate estimate of the kerogen content.