Detecting ahead of bit in a vertical well

Larsen, David; Xavier, J.; Martakov, Sergey; Gripp, J.; Cremonini, R.B.; Pierre, A.; Hinkle, A.; Tropin, Nikita; Sviridov, M.V.; Lagarigue, T.; Antonov, Yuriy

doi:10.3997/2214-4609.201982033

Cited by 3 publications

(2 citation statements)

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“…1) The group of logs denoted as RT compensated resistivity (CRES) represent the traditional resistivity logs included in the deep EM tool (see Figure 1a). The measurements are pro- duced from two transmitter-receiver groups T1 → R2, R1 and T2 → R1, R2 with the symmetric resistivity compensation (Larsen et al, 2019). The phase difference and attenuation of the signals are recorded at 2 MHz and 400 kHz (see Table 1).…”

Section: Synthetic Logs/model Outputsmentioning

confidence: 99%

“…For EDAR, the signals are provided in nV and we use the ± sign to distinguish the maximum signal direction. For DTK, the data from the two receivers of the nonazimuthal signal are used to compensate for any receiver sensitivity variation using special design and proprietary technologies (Larsen et al, 2019); the raw signal in decibel (dB) is used.…”

Section: Synthetic Logs/model Outputsmentioning

confidence: 99%

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Modeling extra-deep electromagnetic logs using a deep neural network

et al. 2021

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Modern geosteering is heavily dependent on real-time interpretation of deep electromagnetic (EM) measurements. We present a methodology to construct a deep neural network (DNN) model trained to reproduce a full set of extra-deep EM logs consisting of 22 measurements per logging position. The model is trained in a 1D layered environment consisting of up to seven layers with different resistivity values.A commercial simulator provided by a tool vendor is utilized to generate a training dataset.The dataset size is limited because the simulator provided by the vendor is optimized for sequential execution.Therefore,we design a training dataset that embracesthe geological rules and geosteering specifics supported by the forward model. We use this dataset to produce an EM simulator based on a DNN without access to the proprietary information about the EM tool configuration or the original simulator source code.Despite employing a relatively small training set size, the resulting DNN forward model is quite accurate for the considered examples: a multi-layer synthetic case and a section of a published historical operation from the Goliat Field.The observed average evaluation time of 0.15 milliseconds per logging position makes it also suitable for future use as part of evaluation-hungry statistical and/or Monte-Carlo inversion algorithms within geosteering workflows.

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Section: Synthetic Logs/model Outputsmentioning

confidence: 99%

Section: Synthetic Logs/model Outputsmentioning

confidence: 99%

Modeling extra-deep electromagnetic logs using a deep neural network

et al. 2021

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Ensemble-based well-log interpretation and uncertainty quantification for well geosteering

et al. 2022

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Hydrocarbon reservoirs are often located in spatially complex and uncertain geological environments, where the associated costs of drilling wells for exploration and development are notoriously high. These costs may be reduced with an optimized well-placement strategy based on real-time geological information, known as well geosteering. To effectively place the well in an updated geomodel and support well geosteering decisions in real-time, we apply an iterative inversion approach based on the Levenberg-Marquardt form of the Ensemble Randomized Maximum Likelihood method. The method estimates geomodel properties together with their uncertainties by reducing the statistical misfit between the measurements acquired with well-logging tools and the predicted measurements from numerical simulations. Analyses of synthetic cases indicate that the method's reliability and computational speed depend on the distance from the logging tool to formation boundaries, the contrast of model properties, and the thickness of formation layers. The proposed method delivers reliable estimates of model properties with only 40 ensemble members and 2--10 iterations, hence it is approximately 10--125 times faster than Metropolis-Hastings Monte Carlo, which we use as a baseline condition given its proven track record. Likewise, the developed method is amenable to parallelization to further reduce computational times.Implementation of the method with a synthetic example inspired by a historical well geosteering operation yielded accurate formation evaluation and verified its accurate and reliable performance under complex geological conditions.

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Optimal Wellbore Placement of a Penta-Lateral Well in the Schrader Bluff Reservoir, North Slope, Alaska

Maraj¹,

Huber²,

Itter³

et al. 2021

SPE Drilling &Amp; Completion

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Summary A significant oil resource exists within the Schrader Bluff and stratigraphic equivalent West Sak reservoirs located on the central North Slope of Alaska. The Schrader Bluff resource is under development in the Kuparuk River Unit, Milne Point, and within the Prudhoe Bay Unit. These areas have developed reservoirs with oil viscosities up to 200 cp under waterflood and viscosity-reducing miscible gas injection. A grass roots penta-lateral gas-lifted producer was drilled and completed to unlock untapped oil in the northern portion of the Polaris S-Pad Schrader Bluff viscous oil reservoir in Prudhoe Bay, Alaska. This producer was the first multilateral well drilled in the Orion and Polaris Schrader Bluff reservoirs in ≈8 years. The team worked to unlock surface and subsurface opportunities, delivering a substantially lower cost of access than planned. Production rate from Schrader Bluff reservoir wells has a positive correlation with contacted net sandstone. Optimal well placement in each of the five laterals was key to the delivery of a successful project. A novel approach to geosteering was used to maximize net sandstone exposure, utilizing deep azimuthal resistivity and real-time user-guided multilayered inversion modeling.

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Detecting ahead of bit in a vertical well

Cited by 3 publications

References 0 publications

Modeling extra-deep electromagnetic logs using a deep neural network

Modeling extra-deep electromagnetic logs using a deep neural network

Ensemble-based well-log interpretation and uncertainty quantification for well geosteering

Optimal Wellbore Placement of a Penta-Lateral Well in the Schrader Bluff Reservoir, North Slope, Alaska

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