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Datir, Harish (Schlumberger) | Kotwicki, Artur (AkerBP) | Venkataramanan, Lalitha (Schlumberger / Schlumberger Doll Research Center) | Best, Kevin (AkerBP) | Tøllefsen, Ingeborg (AkerBP) | Endresen, Tone Riber (AkerBP)
ABSTRACT In 2018, the Frosk prospect was drilled to prove oil in injectite sands. Oil biodegradation was considered to be the main risk based on previous wells in the area. To improve fluid characterization and to analyze the flow potential, Wireline (WL) NMR was utilized in the coring sidetrack and logging while drilling (LWD) NMR on geological sidetrack with two separate passes (drilling and a ream up). Additionally, Triaxial resistivity and dielectric were also logged in the coring sidetrack, to access the petrophysical properties of brecciated sands along with an extensive sampling program. The combined interpretation of all of these measurements: dielectric, resistivity, fluid sampling, NMR shows the value of integrated log interpretation, in turns providing better understanding of the reservoir properties. In the initial assessment, in the coring sidetrack, WL NMR showed short T2 arrival. Later, geological sidetrack was drilled, where LWD time-lapse NMR (ream-up pass) also showed short T2 arrival. Both these data sets suggest that there could be a secondary heavier oil (23 to 25 cP), if those short T2 interpreted as oil. And this heavy oil could be present along with lighter reservoir oil, possibly due to potential secondary charge system. But this finding contradicts the sampled oil PVT analysis, where oil viscosity varies between 6 to 8 cP. An alternative explanation to support short T2 is associated with heavy oil could be that the sampling collects the lighter oil, it is easier to move compared to a heavy, viscous oil. The prior assumption of oil biodegradation, and the alternative scenario of heavy oil presence, influenced the initial NMR interpretation and misled the results. The preliminary WL results delivered were questioned, and they were under scrutiny. When revisited this data, observations made from dielectric, resistivity, and fluid sampling data were overlooked while performing the preliminary standalone NMR interpretation. The Diffusion editing (DE) sequence is useful to separate the formation oil versus oil base mud (OBM) filtrate if there is sufficient viscosity contrast between them (Heaton et al. 2004). DE findings from WL NMR lead by the dielectric, disputed the short T2 arrival caused by heavy oil. It explained that the short T2 was due to the connate water replaced by OBM filtrate. To assess the source of short T2 on LWD NMR, which lacks the DE measurement, a technique called NMR factor analysis (FA) was used (Jain et al 2013). It separates underlying individual poro-fluid distributions and their associated T2LM. The identified oil poro-fluid factor is interpreted and transformed into viscosity. The obtained viscosity from LWD NMR concurs with the re-interpreted WL NMR and PVT results, all three showing oil viscosity very close to 8 cP with ±2 cP. The paper documents a workflow, which provides quality control tools to log analysts and it helps understand the time-lapse effect observed on LWD and WL DE (D-T1-T2) NMR caused by invasion in OBM environment. It also documents the interpretational and measurement limitations on FA and DE. It shows when NMR interpretation lead by dielectric, how it helps resolves the viscosity of biodegraded oil. In the absence of dielectric, it also shows how a flawed interpretation also fits well with the alternative hypothesis of heavier oil left behind after the OBM filtrate invasion. It also documents alternative application via field example, how the integrated log analysis helped re-calibrate the conventional NMR T2 cut-off, when guided by the dielectric. The new T2 cut-off separates the bound and free fluids, enabling to deduce accurate estimates of irreducible water saturation (SwIrr) and improves the permeability estimate without performing a core measurement.
ABSTRACT Many methods of calculating water saturation require knowing the chloride concentration in formation water. The chlorides have a strong effect on the properties of water, and they impact saturation estimates that are based on resistivity, dielectric dispersion, or thermal neutron absorption cross section. In this work, we introduce a new, direct, quantitative measurement of formation chlorine from nuclear spectroscopy, which enables a continuous log of formation water salinity within a limited radial depth. Neutron-capture spectroscopy is sensitive to the presence of chlorine and would be a natural fit for measuring chlorine concentration, if not for the fact that the spectrum contains chlorine gamma rays from both the formation and borehole. The borehole chlorine background can be large and is highly variable from well to well and along depth. Historical efforts to derive water salinity from spectroscopy have relied on ratios of chlorine and hydrogen, which suffer from the presence of the borehole yield and from the hydrogen signal of hydrocarbons. A more reliable basis for salinity interpretation is provided by the direct use of chlorine, once its formation signal has been isolated. We partition the borehole and formation components of chlorine via two unique spectral standards. The contrast between the two standards arises from gamma rays undergoing different amounts of scattering based on their point of origin. The shape of the borehole chlorine standard must be dynamically adjusted along well depth to account for environmentally dependent gamma ray scattering. We represent the borehole standard as a linear combination of two components, with a ratio that is a continuously variable function of borehole size, borehole fluid density, and neutron transport in the formation. The algorithm is derived from a combination of 115 laboratory measurements and 2435 simulated measurements. The modeled database spans a diverse range of lithology, porosity, borehole size, and fluids, and is used to validate the treatment of borehole chlorine. The formation standard describes the remaining chlorine signal, and its yield is readily converted into a log of formation chlorine concentration. The chlorine concentration is useful for multiple petrophysical workflows. In combination with total porosity, chlorine concentration sets a minimum value for water salinity. Adding an organic carbon measurement enables the simultaneous estimation of water volume and water salinity. Chlorine concentration can also be combined with a selected water salinity to compute an apparent water volume for comparison with other methods. Finally, chlorine concentration enables calculation of a maximum expected Sigma, which can be compared with the bulk Sigma log to identify excess thermal absorbers in the matrix. A potential limitation of the measurement is its radial depth of investigation (DOI), which is limited to 8 to 10 in. for 90% of the signal. The chlorine concentration is sensitive to filtrate or connate water, depending on formation permeability and invading fluids. We first present the technique to measure formation chlorine, supported by modeling, laboratory data, and core-log comparisons. We then propose workflows to interpret the formation chlorine concentration in terms of water salinity.
Chen, Hua (Schlumberger) | Sarili, Mahmut (Schlumberger) | Wang, Cong (Schlumberger) | Naito, Koichi (Schlumberger) | Morikami, Yoko (Schlumberger) | Shabibi, Hamed (Petroleum Development Oman) | Frese, Daniela (Petroleum Development Oman) | Pfeiffer, Thomas (Consultant)
ABSTRACT For every barrel of oil, about three to four barrels of water is produced. Water is part of every operation in upstream oil and gas: we produce it, we process it, we inject it. It affects our reserves because it may drive or sweep the oil out of the pores. It is a source of corrosion and scaling in pipe and in the reservoir. Measuring formation water resistivity (Rw) goes beyond using it as the basis of petrophysical well log interpretation. It is the key to telling different waters apart for taking the most representative samples. We introduce a calibrated induction-based water resistivity measurement sensor, which is configured to accurately measure Rw in the flowline of a formation testing tool. The induction-based operating principle of the sensor eliminates the use of electrodes and the associated fouling of the measurement due to coating or accumulation of particles on the electrodes. Instead, the sensor induces an electric current through a nonconductive, neutrally wetting flowline tube that is proportional to the conductivity of the fluid column within the tube. The resulting current at the receiver coil is then converted into resistivity. A case study presents data from a focused water-sampling station in a transition zone in a well drilled with water-based mud (WBM). The resistivity contrast between the mud filtrate and the formation water is low and mobile oil mixes with the formation water and mud filtrate. Despite these difficult conditions, the downhole measurement clearly shows the cleanup progress in real time and compares well with the surface measurements of the water samples. The ability to differentiate formation water from WBM filtrate with low resistivity contrast in the presence of oil places the station depth in the transition zone and enables accurate interpretation of contacts, saturation, and ultimately hydrocarbon in place. The sensor package is suitable for use up to 200-degC temperature and 35,000-psi pressure. The sensor can measure a wide range of resistivity, from 0.01 to 65 ohm.m. Measurements performed on known fluids prove its high accuracy of ±5% or less for resistivity below 10 ohm.m at a resolution of 0.001 ohm.m. The design eliminates any dead volume and all flowline fluid passes through the sensor. The sensor tube is smoothly flushable for fast dynamic response in multiphase slug flow. This paper also discusses optimal sensor placement and operational techniques to achieve best results in multiphase flow environments. The accuracy and resolution of the resistivity measurement enables direct comparison of guard and sample flowlines during focused sampling and provides differentiation even when the contrast between filtrate and formation water is low. The results can serve as a direct Rw measurement, for example in an exploration scenario, as successfully shown in another PDO trial, or can be compared to other sources of Rw measurement or used to improve the accuracy of alternatives to the Archie equation, such as dielectric dispersion.
Simoes, Vanessa (Schlumberger) | Dantas, Marianna (Schlumberger) | Machado, Patrick Pereira (Schlumberger) | Liang, Lin (Schlumberger) | Diogenes, Horrara (Schlumberger) | Duarte, Anna Paula Lougon (Schlumberger) | Abbots, Frances (Shell) | Singhal, Manu (Shell) | Saha, Aloke (Shell)
ABSTRACT Petrophysical characterization includes the evaluation and integration of data from multi-disciplinary well logging measurements. Because of different depths of investigation and multiple physics, each measurement is sensitive to different reservoir properties, including fluid and rock properties. During the well construction, the mud filtrate can invade the near-wellbore region and replace the formation fluids. This may impact density, resistivity, and other measurements. As a result, we may incorrectly estimate porosity and saturation, and therefore underestimate or overestimate reserves. To correct for actual fluid saturation in the pore space, we need to account for mud filtrate properties and the invasion profile. We present here a methodology for using a data and physics-driven method to quantify near-wellbore water mud invasion and to correct the final hydrocarbon volume estimation. Oil and gas reservoirs in Brazil present an average hydrocarbon recovery factor of approximately 21% compared to the world recovery factor of 35%, see Oddone (2018), and for some fields, the biggest challenge in production is associated with extracting the hydrocarbon in rocks that might vary from mixed-wet to strongly oil-wet and that have highly heterogeneity associated to pore connectivity. For that reason, there is an increasing interest in improving the recovery factor and as part of this challenge increasing the understanding of the properties that influence fluid flow including wettability and the connectivity in the pore network. Studying the near-wellbore region with the integration of logs and cores provides an opportunity of improving knowledge about in situ fluid flow and enhancing the estimation of residual oil, better understanding the pore network system and permeability, all of which are important parameters for reservoir characterization and simulation. For water-based mud wells, resistivity and permittivity are sensitive to water saturation variation along with DOI increase and can be acquired with dielectric and resistivity logging tools, which present sensitivity varying from 1 inch to 50 inches away from the borehole wall, information that is the core to extract the radial profile using the methodology. Resistivity and dielectric logs combined with petrophysical properties and core measurements provide the inputs to derive a calibrated water-based mud invasion profile around the borehole. This radial profile of hydrocarbon saturation can be used to correct the traditional petrophysical estimation, improve the final hydrocarbon estimation and provide an estimation of the minimum movable hydrocarbon fraction of the reservoir. By using a data-driven model based on neural network and an extensive training dataset from physics models we built a fast neural network (NN) model that can generate fast and accurate estimations for reservoir analysis. To generate our proxy model, we developed a near well reservoir model in an industry reference reservoir simulator to generate an extensive database of scenarios that could physically represent the saturation changes by mud invasion. A Neural Network was trained in this dataset and was exposed to both unknown synthetic data and well data. The trained NN was able to reproduce the results for predicting the invasion and saturation profile from the reservoir simulator with more than 99% accuracy for all depth of investigation. Additionally, an integrated petrophysical interpretation estimation requires the mud invasion to be calculated several times for each data point on a well, and this task can only be achieved at a reasonable computational cost using the data-driven model, given that the computing for the data-driven model is less than 1% of the same configuration of the physics simulation. Finally, we applied the machine learning proxy model to a complex carbonate well drilled with water base mud. The results showed a good correlation with the resistivity channels from logging measurements for near well and far-field properties and the inverted depth of invasion, as a result of the radial saturation simulation, there is a reduced bias on porosity estimation when compared with a naïve estimation without considering the fluid invasion in density measurement.
ABSTRACT Acquiring physical samples from an open hole is usually a one-opportunity event where a formation tester is sent downhole with a limited number of sample chambers, either on a logging-while-drilling (LWD) or wireline conveyance system. The samples are acquired, retrieved, and sent to a laboratory for analysis, which takes place weeks to months later. By the time the laboratory has performed an analysis, the section has been cemented, and perhaps the rig has finished operations and moved onto the next phase. Success of the sampling operation is predicated on the samples being acquired from the right locations (where to sample?), at the right time to minimize drilling fluid-filtrate contamination (when to sample?), and in a manner that preserves the integrity of the sample and is representative of the formation fluid (how to sample?). Digital sampling is a technique that that can be used to both optimize the when, where, and how of physical samples taken and further augment the information collected with sensor analysis from locations that are not physically sampled. This work shows a new workflow that can be used to extrapolate clean fluid properties with moderately high-contamination levels in a rapid pumpout. Based on the extrapolated clean fluid properties, an operator can make a decision whether to continue the pumpout to obtain physical samples or abort the pumpout if the fluid properties extrapolated (digital sampling) at the location are sufficient for the operation decision making. The workflow starts with applying principal component analysis (PCA) to a multichannel sensor measurement of fluid pumped out of the formation during a formation test sampling operation. Because the fluid pumped out contains only two endmembers (clean formation fluid and mud filtrate), the PCA scores of sensor measurements form a line in the PCA space, and solution bands of endmembers can be estimated based on physical constraint of sensor measurements (non-negative, etc.). Then, a trend-fitting method is used to predict the asymptote of the first principal component score. The asymptote value can be inverted to sensor signal using PCA inversion, and the sensor signal represents the clean formation-fluid measurement. Lastly, machine-learning-based composition models can be used to predict the clean fluid compositions based on the sensor signal. The composition data then is used to predict fluid physical properties, such as bubblepoint, viscosity, and compressibility, using an Equation of State (EOS) model. A series of rapid pumpouts at different depths can be used to map a formation for selection of where to sample, constrain contamination models to improve contamination estimation, determine when to sample, and optimize the pumpout parameters to obtain a representative sample in the shortest period of time. We have applied this workflow to a number of formation sampling jobs at multiple wells, the realtime results match with the laboratory analysis result in term of contamination level and clean fluid properties (compositions, GOR, bubblepoint, density, etc.)