History Matching and Production Forecast with Logs, as Effective Completion and Reservoir Managing Tools in Horizontal and Vertical Wells

Summary Simulation history matching is a daunting, time-consuming task with numerous unknowns and several plausible answers. Scale differences in the data frequently obscure results, limiting its application in completion strategies. Good history matching does not guarantee accurate production forecasts, however. Reliable predictions, required for well planning, depend on the ability of the user to reduce the uncertainties to find consistent and timely solutions. Logs can provide appropriate conditioning data for history matching to enable its use for reservoir management. Electrofacies, capillary pressure, and absolute and relative permeability, imprinted on logs, can be mathematically linked with irreducible water saturation (Swi). Unlike reservoir simulators, well logs are at the right scale for completion designs. Logs facilitate upscaling, honoring rock and fluid properties and the physics of flow (Haro 2006). Logs are snapshot measurements that are amenable for conversion into dynamic forecasting tools by use of flow and pressure equations. This concept permits creation of synthetic production logs (SPLTs) over time, from which production decline can be calculated. This method consists of integrating material balance, flow/ pressure algorithms, fluid data, cores, and log data. Thus, the corresponding analytical expressions are required. In this approach, every well represents a finite, gridded tank, capable of producing a measurable volume of fluids, limited by its petrophysical constraints. Superposition, in terms of pressure and flow, combines the various components within and among wells. The quality of the results is ensured because material balance must be honored at every depth at all times under different production scenarios and the prevailing drive mechanism. This log-handling technique helps when making strategic economic decisions to maximize reserves and optimize the reservoir-development plan. This strategy is used to obtain oil in place (OIP), drainage radii, lateral connectivity, fluid-bank arrival times, productivity indices (PIs), inflow performance relationship (IPR), production allocation, and recovery per zone per well. Current log analyses or simulators generally do not provide these parameters at this detail. This refined use of logs streamlines completion designs and improves conformance, enabling us to comply with an important part of daily reservoir management.

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Effect of Scaling Parameters on Waterflood Performance with Horizontal and Vertical Wells

Energy & Fuels ◽

10.1021/ef070097b ◽

2008 ◽

Vol 22 (1) ◽

pp. 402-409 ◽

Cited By ~ 11

Author(s):

Nanji J. Hadia ◽

Lalit S. Chaudhari ◽

Sushanta K. Mitra ◽

Madhu Vinjamur ◽

Raghuvir Singh

Keyword(s):

Vertical Wells ◽

Scaling Parameters ◽

Horizontal And Vertical Wells

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Characteristics of Dimensionless Pressure Gradients and Derivatives of Horizontal and Vertical Wells Completed within Inclined Sealing Faults

10.2118/207179-ms ◽

2021 ◽

Author(s):

A V Ogbamikhumi ◽

E S Adewole

Keyword(s):

Pressure Drop ◽

Horizontal Well ◽

Superposition Principle ◽

Early Time ◽

Pressure Gradients ◽

Vertical Wells ◽

Pressure Derivative ◽

Vertical Well ◽

Dimensionless Pressure ◽

Horizontal And Vertical Wells

Abstract Dimensionless pressure gradients and dimensionless pressure derivatives characteristics are studied for horizontal and vertical wells completed within a pair of no-flow boundaries inclined at a general angle ‘θ’. Infinite-acting flow solution of each well is utilized. Image distances as a result of the inclinations are considered. The superposition principle is further utilized to calculate total pressure drop due to flow from both object and image wells. Characteristic dimensionless flow pressure gradients and pressure derivatives for the wells are finally determined. The number of images formed due to the inclination and dimensionless well design affect the dimensionless pressure gradients and their derivatives. For n images, shortly after very early time for each inclination, dimensionless pressure gradients of 1.151(N+1)/LD for the horizontal well and 1.151(N+1) for vertical well are observed. Dimensionless pressure derivative of (N+1)/2LD are observed for central and off-centered horizontal well locations, and (N+1)/2 for vertical well are observed. Central well locations do not affect horizontal well productivity for all the inclinations. The magnitudes of dimensionless pressure drop and dimensionless pressure derivatives are maximum at the farthest image distances, and are unaffected by well stand-off for the horizontal well.

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Caveats and Pitfalls of Production Forecast Uncertainty Analysis Using Design of Experiments

10.2118/203919-ms ◽

2021 ◽

Author(s):

Boxiao Li ◽

Hemant Phale ◽

Yanfen Zhang ◽

Timothy Tokar ◽

Xian-Huan Wen

Keyword(s):

Design Of Experiments ◽

Uncertainty Analysis ◽

History Matching ◽

Petroleum Industry ◽

Production Forecast ◽

Forecast Uncertainty ◽

Field Development ◽

Development Alternatives ◽

Modeling Principles ◽

Made In

Abstract Design of Experiments (DoE) is one of the most commonly employed techniques in the petroleum industry for Assisted History Matching (AHM) and uncertainty analysis of reservoir production forecasts. Although conceptually straightforward, DoE is often misused by practitioners because many of its statistical and modeling principles are not carefully followed. Our earlier paper (Li et al. 2019) detailed the best practices in DoE-based AHM for brownfields. However, to our best knowledge, there is a lack of studies that summarize the common caveats and pitfalls in DoE-based production forecast uncertainty analysis for greenfields and history-matched brownfields. Our objective here is to summarize these caveats and pitfalls to help practitioners apply the correct principles for DoE-based production forecast uncertainty analysis. Over 60 common pitfalls in all stages of a DoE workflow are summarized. Special attention is paid to the following critical project transitions: (1) the transition from static earth modeling to dynamic reservoir simulation; (2) from AHM to production forecast; and (3) from analyzing subsurface uncertainties to analyzing field-development alternatives. Most pitfalls can be avoided by consistently following the statistical and modeling principles. Some pitfalls, however, can trap experienced engineers. For example, mistakes made in handling the three abovementioned transitions can yield strongly unreliable proxy and sensitivity analysis. For the representative examples we study, they can lead to having a proxy R2 of less than 0.2 versus larger than 0.9 if done correctly. Two improved experimental designs are created to resolve this challenge. Besides the technical pitfalls that are avoidable via robust statistical workflows, we also highlight the often more severe non-technical pitfalls that cannot be evaluated by measures like R2. Thoughts are shared on how they can be avoided, especially during project framing and the three critical transition scenarios.

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Integrated Production Model as a Tool for Optimization the Development Strategy of the Sakhalin Oil and Gas Condensate Field

10.2118/208502-ms ◽

2021 ◽

Author(s):

Oleksandr Doroshenko ◽

Miljenko Cimic ◽

Nicholas Singh ◽

Yevhen Machuzhak

Keyword(s):

History Matching ◽

Effective Field ◽

Production Optimization ◽

Production Model ◽

Production Forecast ◽

Hydrocarbon Production ◽

Integrated Production ◽

Wellhead Pressure ◽

Field Development ◽

The Impact

Abstract A fully integrated production model (IPM) has been implemented in the Sakhalin field to optimize hydrocarbons production and carried out effective field development. To achieve our goal in optimizing production, a strategy has been accurately executed to align the surface facilities upgrade with the production forecast. The main challenges to achieving the goal, that we have faced were:All facilities were designed for early production stage in late 1980's, and as the asset outdated the pipeline sizes, routing and compression strategies needs review.Detecting, predicting and reducing liquid loading is required so that the operator can proactively control the hydrocarbon production process.No integrated asset model exists to date. The most significant engineering tasks were solved by creating models of reservoirs, wells and surface network facility, and after history matching and connecting all the elements of the model into a single environment, it has been used for the different production forecast scenarios, taking into account the impact of infrastructure bottlenecks on production of each well. This paper describes in detail methodology applied to calculate optimal well control, wellhead pressure, pressure at the inlet of the booster compressor, as well as for improving surface flowlines capacity. Using the model, we determined the compressor capacity required for the next more than ten years and assessed the impact of pipeline upgrades on oil gas and condensate production. Using optimization algorithms, a realistic scenario was set and used as a basis for maximizing hydrocarbon production. Integrated production model (IPM) and production optimization provided to us several development scenarios to achieve target production at the lowest cost by eliminating infrastructure constraints.

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