Best Practices in Reservoir Management

When tasked with history matching a large and/or complex reservoir model, utilizing an "Assisting History Match" tool can significantly enhance the efficiency and effectiveness of the process, leading to improved production profile matches within a reasonable timeframe. In my experience, I advocate for employing a comprehensive set of variables in my analysis to fine-tune the model and achieve optimal results. Typically, I start by adjusting global effective parameters, which include global and directional permeability adjustments, a single Kv/Kh ratio, a unified set of saturation functions, fixed PVT (Pressure-Volume-Temperature) properties, consistent grid cell connectivity, overall fault transmissibility, fluid contacts, and aquifer extent, among others. Once the global parameters are established, I progress to the second level of analysis, where I apply similar variables on a more localized scale. This includes focusing on well regions or individual rock types. At this stage, I also incorporate additional parameters such as adjustments to pore volumes, localized Kv/Kh ratios relevant to specific well regions or rock types, and identification of individual high-permeability streaks The third level of the process combines all previously mentioned variables with the introduction of multiple relative permeability (Kr’s) curve shapes for each rock type. These Kr curves are instrumental in capturing different flow behaviors, including strong, medium, or weak flow, and they account for changes in wettability conditions with depth, such as oil-wet, mixed-wet, or water-wet systems. It is widely acknowledged that increasing the number of simulation runs enhances the probability of achieving better history match outcomes. Additionally, simplifying the number of discrete parameters can facilitate a more effective assessment of the impact of various variables on the model. This systematic approach not only aids in reaching a successful match but also provides insights into the underlying reservoir dynamics.

💡Applying Unconventional Fracturing to Conventional Reservoirs: Opportunity with Caution The last decade of shale development has completely reshaped hydraulic fracturing practices. High-rate pumping, closely spaced clusters, aggressive diversion strategies and real-time optimization have become standard in unconventional plays. More and more operators are now starting to transfer these techniques to conventional reservoirs — and the results have been promising. In fields facing production decline, compartmentalization or thin pay zones, unconventional-style completions have helped unlock incremental reserves and extend asset life. That said, applying these practices outside of shale environments requires careful consideration. Conventional reservoirs behave very differently and the “copy-paste” approach rarely delivers sustainable value. Success depends on adapting the methodology to the specific formation, rather than simply increasing stage count or fluid volume. • Permeability and rock heterogeneity: In higher-permeability zones, fracture extension and proppant placement can become less predictable. Long fractures may not necessarily translate into better coverage without a strong diversion strategy. • Cluster efficiency: Unlike shales, conventional formations tend to develop dominant fractures. Without proper stage isolation or temporary plugging, energy may concentrate near the heel and leave other clusters unstimulated. • Fluid and proppant selection: Slickwater systems used in shale often result in narrow fractures that close quickly in higher-permeability rock. Hybrid or crosslinked systems may be a better fit — but come with higher friction and crosslinker-sensitivity. • Stress interaction and depletion: When applying multi-stage techniques in mature fields, reservoir depletion can lead to pressure sinks and complex stress shadows that negatively affect fracture geometry if not properly modelled. • Economic calibration: There is a risk of “over-stimulation.” The incremental barrels from a more aggressive job still need to justify additional cost, especially where base decline rates are steep. The way I see it, unconventional techniques can bring significant value to conventional assets — but only if applied through a fit-for-purpose design. The real opportunity lies in combining unconventional operational discipline (design → execute → learn → redesign) with a fundamental understanding of the conventional reservoir. When these two worlds meet, the impact is substantial. #HydraulicFracturing #ReservoirEngineering #ConventionalReservoirs #UnconventionalTechniques #Stimulation #OilAndGas #FracDesign #CompletionEngineering #Innovation

Volumetric Method Principle: Estimates hydrocarbons in place (STOIIP/GIIP) based on the reservoir’s geometry, porosity, saturation, and formation volume factor. Applies before production begins (static method). Strengths: Useful in early field life (before production data). Straightforward and quick. Requires geological and petrophysical data. Weaknesses: Accuracy depends on data quality (porosity, thickness, area). Assumes uniformity—doesn't capture heterogeneity or compartmentalization. Does not account for reservoir connectivity. 🔍 2. Material Balance Method (MBE) Principle: Uses the law of conservation of mass to estimate Original Hydrocarbon in Place (OHIP) by relating cumulative production to pressure depletion. Strengths: Applicable after some production data is available. Good for estimating drive mechanisms. Integrates PVT and production data. Weaknesses: Assumes average reservoir pressure is known accurately. Requires reliable PVT data. Sensitive to aquifer behavior assumptions. 🔍 3. Decline Curve Analysis (DCA) Principle: Projects future production using historical trends (rate-time data), assuming reservoir behavior remains consistent. Types include: Exponential Harmonic Hyperbolic Strengths: Simple and fast. Requires only production data. Effective in mature reservoirs. Weaknesses: Poor prediction in early life or unstable production. Doesn’t directly estimate hydrocarbons in place. Assumes constant operating conditions and no interventions. 🔍 4. Reservoir Simulation (Numerical Modeling) Principle: Uses mathematical models and computer simulations to predict reservoir performance under different scenarios. Integrates geology, petrophysics, PVT, SCAL, and production history. Strengths: Handles complex reservoir geometries. Simulates different development strategies. Powerful for optimization and forecasting. Weaknesses: Data- and labor-intensive. Requires skilled personnel and calibration. Can produce misleading results if poorly constrained. 🔍 5. Analog/Analytical Models Principle: Estimates reserves by comparing with similar, previously developed fields (analogs). Strengths: Quick and low cost. Useful for frontier areas with little data. Weaknesses: Assumes similarity—can be misleading. Not suitable for unique or heterogeneous reservoirs. 🔍 6. Probabilistic Methods (Monte Carlo Simulation) Principle: Applies probability distributions to input variables (porosity, saturation, area, etc.) to generate a range (P90, P50, P10) of reserves. Strengths: Accounts for uncertainty. Provides risk-based estimates. Useful for decision-making and portfolio management. Weaknesses: Requires proper input distributions. Computational resources needed. Can give false confidence if assumptions are wrong.