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We propose an approach to inversion of induction LWD measurements based on calculation of the synthetic signals by artificial neural networks (ANN) specially trained on some database. The database for ANN training is generated by means of the proprietary 2D solver Pie2d. Validation of the proposed approach and estimation of computation time is performed for the problem of reconstruction of the three–layer model with a wall. Also, we make uncertainty analysis for the reconstructed model parameters for two tool configurations.

Reliable estimates of the fluid pressure in the pore space of rocks are critical for different aspects of petroleum exploration and production including injection operations and scenarios of water flooding. Numerous approaches are available for formation pore pressure evaluation, however, these measurements become a challenge inside a cased borehole, and a list of possible options is short: either the casing is to be perforated, or the production tubing needs to be disconnected to perform the pressure tests. We present a method for through-casing evaluation of formation pore pressure without shutting down production. We suggest equipping an observation well with a borehole gravimeter and acquiring time variations of the vertical component of the gravity field. Changes in gravity occur during gas production and are related to time variations of formation pore pressure. Gravity changes obtained in the observation well are supposed to be inverted for time-dependent formation pore pressure variations beyond the casing. Our results and recommendations are based on numerical modeling of pore pressure spatial distribution during gas field exploitation and relevant changes in borehole gravity. Benchmark models were elaborated in order to consider a dynamic process of pressure changes in time and space under conditions similar to those in the Medvezhye gas field (Russia). Different modeling scenarios are considered for early and late stages of gas field exploitation. The sensitivity analysis was performed to estimate quantitatively a sensitivity of borehole temporal gravity changes to variations in formation pore pressure behind the casing. Based on resolution analysis we justify the possibility to extract the gravity measurements directly related to changes in pore pressure from the total changes in the gravity field due to reservoir exploitation. The impact of pore pressure on the gravity field measured in boreholes during the water flooding is also evaluated, and obtained results are discussed.

In most cases, interpretation of resistivity measurements is performed using 1D multilayered formation models that are used to fit data locally in real-time applications. While drilling high-angle or horizontal wells, more complex scenarios may occur, such as faults, pinch-outs, or unconformities. In these cases, resistivity logging data inversion should be performed using at least a 2D model, which is a more complex computational problem. This paper presents a neural networks approach for solving this problem exemplified by the application of a deep azimuthal resistivity tool for geosteering in the vicinity of a tectonic fault. The tool operational frequencies of 400 kHz and 2 MHz produce eight measurements with a coaxial arrangement of transmitters and receivers, and two azimuthally sensitive measurements with axial transmitters and transverse receivers. This paper considers a 2D model of a tectonic fault composed of three parallel layers on the one side of a displacement plane and the same three layers on the other side dislocated at a certain distance along the displacement plane. The model is described with nine independent parameters. The artificial neural networks (ANNs) were designed and trained to calculate the tool signals based on the model parameters. The training was carried out using a synthetic database of 4·105 elements containing the model parameters and corresponding tool signals. The database was calculated using distributed computations with in-house Pie2D software that used the boundary integral equation technique. To estimate the accuracy of the ANNs designed, the signals calculated with the networks were compared against the exact values obtained with Pie2D for an independent sample of 1.8·104 points. The comparison gave a good match for all 10 measurements, with the relative error comprising less than one standard tool measurement error for most points of the sample. Computation with the ANN required a few microseconds to calculate one signal, while the algorithm based on boundary integral equations required several minutes. The obtained acceleration of ~106 indicates many opportunities for modeling and inversion of logging-while-drilling data.

Abstract Traditional gravimetry applications are mining/oil exploration surface gravity and formation bulk density borehole gravity logging. Large-scale reservoir saturation monitoring is a new gravimetry application. Substitution of oil or gas by water leads to density changes in large reservoir volumes, which causes time dependent subsurface and surface gravity field variations. This case study presents a complex multilayered reservoir time-lapse gravity data inversion problem. The customary bitmap approach requires many input parameters with a well-known inversion ambiguity. This ambiguity is in this work reduced by introducing a priori information obtained by biasing the inversion with history matched reservoir simulation output. A synthetic gravity data set was first generated by forward gravity modeling using the simulation saturation output from an onshore giant Middle Eastern oil field. By analyzing the simulation saturation data, the reservoir layer specific behavior of the water saturation and oil-water flood front was understood and used as a priori input in the optimized inversion algorithm used to fine-tune the predicted location of the oil-water flood front on the basis of the synthetic gravity data. Numerical examples with associated graphics demonstrate how inversion and accuracy estimates work for this data set. The proposed inversion technique will depict differences from the history matched simulation saturation and the gravity data; therefore, actual field gravity data will allow the enhancement of the reservoir simulation history match precision. The presented inversion of time-lapse gravity data demonstrates a substantial potential for the 4D microgravity. Since borehole gravity sensors are less affected by near surface changes, the borehole gravity data has significantly improved spatial resolution and much higher measured ? in the gravity signal resulting from the fluid substitution of hydrocarbons by water. In the presented inversion work, the maximum synthetic time-lapse ? gravity signal at the top reservoir was 18 times greater in magnitude compared to the predicted maximum surface time-lapse gravity signal amplitude. For microgravity to be a serious alternative for inter-well hydrocarbon saturation mapping for oil fields under waterflood, the borehole microgravity hardware development needs to be given significant R&D priority to reduce the diameter of the borehole gravity tool and improve gravity meter precision for both surface and borehole sensors. Introduction Geology and reservoir engineering describe the subsurface reservoir by utilizing sparse well data. As only a very small fraction of the subsurface can be observed through cores, well logs and well production data, the inter-well reservoir characterization is very challenging, especially in heterogeneous or fractured reservoirs, where simply interpolating wellbore data is not adequate to infer fluid distribution. At present, dynamic changes between wellbore control points is only understood via matching reservoir simulation models to reproduce the acquired well data. In the Journal of Petroleum Technology (JPT) May 2011 issue, the Society of Petroleum Engineers (SPE) Research & Development (R&D) Committee defined the five grand R&D challenges facing the oil and gas industry (Judzis et al. 2011), which included higher resolution subsurface imaging of hydrocarbons. In a follow-up JPT article, Neal and Krohn (2012) identified the most advanced technology options offering a solution to the inter-well hydrocarbon mapping problem as:3D/4D Seismic: surface, borehole and cross-well acquisition;Electromagnetic: borehole to surface and crosswell;Microgravity: borehole and surface acquisition; andNanotechnology.

Gravimetry is a physical method with a large depth of investigation. Traditional applications include surface gravity observations for mining and oil exploration and borehole gravity logging for investigating formation bulk density. A new application of gravimetry is large-scale reservoir saturation monitoring. Replacement of oil or gas by water leads to density changes in large volumes of the reservoir, which causes changes of the gravity field down hole as well as on the surface. Since borehole gravity sensors are closer to the reservoir than for surface acquired gravity data, borehole gravity data has better spatial resolution and are less affected by near surface changes. This paper focuses on the problems of inversion of time-lapse gravity data for complex multilayered reservoirs and estimation of the accuracy of the reconstructed oil-water flood front. The traditional bitmap approach (dividing the reservoir into blocks) requires a huge number of parameters and leads to the well-known inversion ambiguity. This ambiguity can be reduced by introducing a priori information. The basic idea of the presented approach is to obtain this a priori information by biasing the inversion with output from a history matched reservoir simulation data set. In this case, reservoir simulation saturation data from an onshore giant Middle Eastern oil field was used as input. By processing the simulation saturation data, it was possible to understand the behavior of the water saturation and oil-water flood front in the different layers of the reservoir. Using this knowledge, a 3D model of density changes was introduced. This model formed the basis of the optimization inversion algorithm used to fine-tune the actual location of the oil-water flood front on the basis of gravity data. Numerical examples demonstrate how inversion and accuracy estimates work for data obtained from a realistic reservoir simulation. The proposed inversion technique will depict any differences from the history matched reservoir simulation saturation output and the gravity data; thus, the gravity data will allow enhanced precision of the reservoir simulation history match.