Most unconventional gas reservoirs are naturally fractured in nature and exhibit dual porosity characteristics. Hydraulic fracturing often alters the reservoir parameters around the wellbore, thus, potentially creating a rubble zone (stimulated reservoir volume-SRV) with distinctly different characteristics when compared to the outer zone. This problem could ideally be approximated as an equivalent flow problem around a horizontal wellbore in a composite naturally fractured domain. The computational package developed in the current study could be used in generating forward solutions for prediction of production transients in hydraulically fractured double porosity reservoirs. Additionally, as a part of an inverse analysis procedure, using relevant dimensionless parameters, it will be possible to characterize the composite naturally fractured reservoirs. A solution to the elliptical flow problem that considers flow into a horizontal wellbore in a truly composite naturally fractured reservoir has been attempted. Mathieu modified functions were used to solve the elliptical flow problem. Stehfest algorithm is used for inversion of the Laplace space solutions back to real time domain. This generated solution is validated with other existing solutions by collapsing it into its subsets given in the literature. Forward solutions are generated for various dimensionless parameters. A graphic user interface (GUI) has been developed to generate production decline curves. The interface elliptical coordinate does have a significant effect on the dual porosity signature of production transients in the case of mobility ratios higher than 10. It is observed that the mobility ratio, diffusivity ratio, storativity ratio, interporosity flow coefficient ratios of the inner, and outer regions exhibit significant effects on the decline curves experienced by this class of reservoirs.

In early stages of reservoir depletion, it is often a challenging task to accurately determine reservoir properties that are representative of the actual field. Due to different scales of data obtained from various sources like seismic data, well logs, cores, and production data, there is a lot of uncertainty in solving the inverse problem of estimating formation rock and fluid properties from the field data. Hard-computing protocols like reservoir simulation are time and labor intensive. The objective of the current study is to develop a reservoir characterization tool using a novel approach of correlating seismic attributes with well logs and production data using artificial intelligence approach. The tool will enable construction of spatial oil maps at different times revealing sweet spots and aid in optimized field development planning. A workflow is developed for devising a comprehensive reservoir characterization tool based on artificial expert systems. A case study of an offshore deep-water asset is used in demonstrating the tenets of the workflow. The reservoir under consideration is highly heterogeneous in terms of property distribution and is believed to be highly channelized. The ANN based tool assists in identifying sweet spots by predicting optimal well location/completion parameters and production profiles. The multilayer feedforward back-propagation based neural network tool developed is able to capture the correlations that exist amongst seismic data, well logs, completion data, and production data. Well logs are correlated to seismic attributes and geometric location of wells with an average testing (blind test) error of less than 20%. Having correlated seismic data with well logs, synthetic well logs are generated for the entire area of seismic coverage. Synthetic well logs combined with seismic data are able to correlate well with the production within 21% error. The tool developed enables users to predict entire well log suites for even a directional well of user defined configuration through a graphic user interface in a short period of time (typically less than a minute). This methodology uses a unique way of computing seismic attributes following a horizontal well path and correlating them with the suite of well logs. Incorporation of interference effect from neighboring producers and injectors, schedule of production and functional links based on geographic location has made the production performance module robust and reliable. The workflow enables generation of oil production forecast maps through production performance network. NPV (net present value) calculations integrated with production forecasts is used in identifying the potential infill well locations. The results discussed in the paper showcase the robust nature of the methodology.

Summary Standard history-matching workflows use qualitative 4D seismic observations to assist in reservoir modeling and simulation. However, such workflows lack a robust framework for quantitatively integrating 4D seismic interpretations. 4D seismic or time-lapse-seismic interpretations provide valuable interwell saturation and pressure information, and quantitatively integrating this interwell data can help to constrain simulation parameters and improve the reliability of production modeling. In this paper, we outline technologies aimed at leveraging the value of 4D for reducing uncertainty in the range of history-matched models and improving the production forecast. The proposed 4D assisted-history-match (4DAHM) workflows use interpretations of 4D seismic anomalies for improving the reservoir-simulation models. Design of experiments is initially used to generate the probabilistic history-match simulations by varying the range of uncertain parameters (Schmidt and Launsby 1989; Montgomery 2017). Saturation maps are extracted from the production-history-matched (PHM) simulations and then compared with 4D predicted swept anomalies. An automated extraction method was created and is used to reconcile spatial sampling differences between 4D data and simulation output. Interpreted 4D data are compared with simulation output, and the mismatch generated is used as a 4D filter to refine the suite of reservoir-simulation models. The selected models are used to identify reservoir-simulation parameters that are sensitive for generating a good match. The application of 4DAHM workflows has resulted in reduced uncertainty in volumetric predictions of oil fields, probabilistic saturation S-curves at target locations, and fundamental changes to the dynamic model needed to improve the match to production data. Results from adopting this workflow in two different deepwater reservoirs are discussed. They not only resulted in reduced uncertainty, but also provided information on key performance indicators that are critical in obtaining a robust history match. In the first case study presented, the deepwater oilfield 4DAHM resulted in a reduction of uncertainty by 20% of original oil in place (OOIP) and by 25% in estimated ultimate recoverable (EUR) oil in the P90 to P10 range estimates. In the second case study, 4DAHM workflow exploited discrepancies between 4D seismic and simulation data to identify features necessary to be included in the dynamic model. Connectivity was increased through newly interpreted interchannel erosional contacts, as well as subseismic faults. Moreover, the workflow provided an improved drilling location, which has the higher probability of tapping unswept oil and better EUR. The 4D filters constrained the suite of reservoir-simulation models and helped to identify four of 24 simulation parameters critical for success. The updated PHM models honor both the production data and 4D interpretations, resulting in reduced uncertainty across the S-curve and, in this case, an increased P50 OOIP of 24% for a proposed infill drilling location, plus a significant cycle-time savings.

Demonstrating the viability of multistage hydraulic fractured horizontal wells to unlock otherwise trapped resources is presented through a case study on Rangely. A combination of high-fidelity reservoir models was employed for accurate forecasts and evaluation of hydraulically fractured horizontal wells to improve resources in this mature conventional oil field with ongoing pressure support and tertiary recovery operations. The modeling techniques used in this method can be extended to other mature oil fields to unlock bypassed oil setting a precedent to re-evaluate mature oil fields with the new unconventional completion technologies. The Rangely Weber Sand Unit is an Eolian sandstone depositional system consisting of 2 billion bbls of oil in place. The Weber Formation is Pennsylvanian to Permian in age, and typically consists of fine-grained and cross bedded calcareous sandstones. Structurally oil is trapped in an anticline with varying dip angles on the flanks. The oil production from this reservoir was managed through primary depletion for the first two decades of production followed by secondary recovery via water flood and concluding through water alternating CO2 injection (WAG) over the last three decades. Due to the heterogeneity in depositional environment, the recovery factors have been low in the eastern end of the field. The east end of the field has relatively lower permeability and lower porosity compared to the rest of the field. A modeling workflow is presented to assist with evaluation and optimization of hydraulically fractured horizontal infill wells to recover bypassed oil in the eastern end of the Rangely field. A full fidelity static model was built based on dense, high quality well control data. A sector model was history matched, and then used to update pressure, saturations, and stress distribution to present day. The history matched model was subsequently used to evaluate horizontal well performance and hydraulic fracturing completion options to overcome these heterogeneities and improve recovery from a lower quality reservoir. Completions optimization opportunities were focused on fracture geometry, incremental Estimated Ultimate Recovery (EUR), and economics. Sensitivity studies demonstrated that an optimal balance of cost and recovery is found at the low end of fracture volumes and wider perforation cluster spacing. Forecasting runs show incremental economic recovery which otherwise could not have been recovered through ongoing WAG operations.

Standard history matching workflows use qualitative 4D seismic observations to assist in reservoir modeling and simulation. However, such workflows lack a robust framework for quantitatively integrating 4D seismic interpretations. 4D or time-lapse seismic interpretations provide valuable inter-well saturation and pressure information and quantitatively integrating this inter-well data can help to constrain simulation parameters and improve the reliability of production modeling. This paper outlines technologies aimed at leveraging the value of 4D for reducing uncertainty in the range of history matched models and improving the production forecast. The proposed 4D Assisted History Match (4DAHM) workflows utilize interpretations of 4D seismic anomalies for improving the reservoir simulation models. Design of Experiments (DOE) is initially used to generate the probabilistic history match simulations by varying the range of uncertain parameters. Saturation maps are extracted from the Production History Matched (PHM) simulations and then compared with 4D predicted swept anomalies. An automated extraction method was created and is used to reconcile spatial sampling differences between 4D data and simulation output. Interpreted 4D data is compared with simulation output, and the mismatch generated is used as a 4D filter to refine the suite of reservoir simulation models. The selected models are used to identify reservoir simulation parameters that are sensitive for generating a good match. The application of 4DAHM workflows has resulted in reduced uncertainty in volumetric predictions of oil fields, probabilistic saturation S-curves at target locations, and fundamental changes to the dynamic model needed to improve the match to production data. Results from adopting this workflow in two different deep-water reservoirs are discussed. They not only resulted in reduced uncertainty, but also provided information on key performance indicators that are critical in obtaining a robust history match. In the first case study presented, the deep-water oil field 4DAHM resulted in a reduction of uncertainty by 20% in OOIP and by 25% in EUR in the P90-P10 range estimates. In the second case study, 4DAHM workflow exploited discrepancies between 4D seismic and simulation data to identify features necessary to be included in the dynamic model. Connectivity was increased through newly interpreted inter-channel erosional contacts, and sub-seismic faults. Moreover, the workflow provided an improved drilling location which has the higher probability of tapping unswept oil and better EUR. The 4D filters constrained the suite of reservoir simulation models and helped to identify 4 out of 24 simulation parameters critical for success. The updated PHM models honor both the production data and 4D interpretations, resulting in reduced uncertainty across the S-curve and, in this case, an increased P50 OOIP of 24% for a proposed infill drilling location, plus a significant cycle-time savings.