As shale oil production matures, widespread observations of negative divergence from industry forecasts are being reported by major industry and financial news outlets. Such revelations have strained investor confidence and led to severely restricted capital markets and heightened financial hurdles. Regaining investor confidence and access to capital requires demonstrating significantly improved development proficiency.
And step one is generating more reliable forecasts for setting achievable expectations.
1. Decline Curve Analysis with integrated GOR [iGOR-DCA™]
Arps’ Decline Curve Analysis (DCA) remains a popular forecast methodology due to its practical, empirical nature and minimal data requirements. Often criticized for its geological origins in conventional reservoirs, traditional DCA lacks an innate mechanism for predicting the critical transitions in reservoir behavior known to dramatically affect decline severity.
With our seven-year head start anticipating and modeling GOR trend escalations and their correlation with negative divergence from legacy forecasts, Shale Specialists has successfully integrated GOR trend analysis into a familiar DCA processes to enable unprecedented, early-time prediction of critical transitions in reservoir decline behavior. The result is iGOR-DCA™, a fully patented, easy-to-use, DCA technology that dramatically improves the reliability of early-time forecasts. Contact us for an in-house demonstration and licensing options.
iGOR-DCA™ on daily data:
iGOR-DCA™ on public monthly data:
2. Predictive Material Balance™ for the Pre-Determination of Optimal Well Spacing
Determining well spacings that minimize interference while maximizing per-well and per-drilling unit oil recoveries can be capital-destructive if left to trial and error experimentation. Our Predictive Material Balance™, refined over a 7-year headstart isolating and quantifying primary reservoir drive energy unique to stimulated shale reservoirs, combines our unrivaled advanced determination of available recovery factor (Pro-RFM™) with our enhanced precision OOIP mapping, to determine Recoverable Oil-in-Place (ROIP™) available in a given drilling unit.
Advanced knowledge of oil-in-place recoverable from primary methods enables optimization of well spacing, well recoveries, and stimulation efficiency.
3. Stimulated Reservoir Volume Dimensional Analysis [SRV-DA™] for the Determination of Implied Well Spacing
Determining how close to offset an existing producer is a critical decision operators face every day. Drill too close and both wells can fall short of expectations. Space too wide and you leave valuable resources behind. We combine advancements in hydrocarbon-filled porosity uncertainty minimization (HCFP+™) with our Proactive Recovery Factor Modeling (Pro-RFM™) and enhanced reliability forecasts (iGOR-DCA™ ) to determine a Stimulated Oil Volume (SOV) directly from production.
Carefully constrained fluid and reservoir properties are used to convert an SOV into an Implied SRV. Completion reports, and various mechanical analysis methods are used to resolve height and width dimensions of complex SRV shapes to assist with determining how close to vertically and horizontally offset the existing producer.
4. Proactive Recovery Factor Modeling [Pro-RFM™]
TOTAL RECOVERY FACTOR BASED ON PRIMARY RESERVOIR DRIVES’ ABILITY TO PERFORM THE WORK OF HOISTING TONS OF OIL TO THE SURFACE AS PRODUCTION
Elevate recovery factor from a passive ‘sanity check’ to a primary, proactive tool determined from first principles and entirely independent of production data or OOIP. Leverage our seven-year head start analyzing and mapping the capacity of primary reservoir drives to perform the work of hoisting tons of oil to the surface. Transform one of shale’s most challenging features - infinitesimally low native permeability - into a powerful analytical solution.
Traditional reservoir simulation using standard flow-through-porous-media concepts requires simultaneous solution for both ‘how much oil will be produced’ and ‘how long it will take’. Pro-RFM™ provides a time-independent, standalone solution for ‘how much oil will be produced’, literally eliminating permeability and viscosity from the equations. While the time component is clearly important, a de-parameterized solution focused solely on drive mechanism work capacity is easier to constrain and advantageous to uncertainty minimization.
sdfsdf
Recoverable Oil-in-Place Mapping
Combining our revolutionary Proactive Recovery Factor Modeling (Pro-RFM™ ) with our Integrated Analysis to Minimize Uncertainty in HCPV & OOIP generates the ultimate treasure map: recoverable oil-in-place (ROIP™) available from primary reservoir drives. Use it to realistically estimate acreage, asset and company value or to design the ultimate development program to achieve exceptional financial returns.
5. Hydrocarbon-Filled Porosity Uncertainty Minimization Core Reprocessing (HCFP+™)
What is the best measurement to validate the hydrocarbon content of your reservoir? There are many different measurements types to choose from yet they seldom all agree. Simply choosing the one with the highest value can lead to poor correlations and unreliable performance predictions from otherwise fundamentally intuitive relationships.
Key questions to ask:
Has the core been corrected for mechanical stress induced by exhumation?
Has the core been corrected for thermal stress induced by the measurement process itself?
With SSLLC’s unique experience and special core correction processing, we can re-process your existing core data to reveal unparalleled agreement between disparate measurements that can reinvigorate the predictive capability of fundamentally intuitive relationships.
6. Integrated Analysis to Minimize Uncertainty in HCPV & OOIP
Building on improvements in core processing techniques, multiple independent petrophysical, geochemical and reservoir engineering workflows are calibrated to new, more closely agreeing measurement values to ‘triangulate’ a narrower range in which actual hydrocarbon-filled porosity must lie. Expanding the geochemical and petrophysical workflows’ natural overlap with reservoir engineering (ie. relationships between iGOR, iAPI, HI, and VRo% equivalent), HCPV is converted into OOIP and OGIP as an intrinsic part of the process as one, integrated workflow generates key reservoir fluid properties.
7. PRY™ Analysis: Rethinking Frac Height Modeling and Landing Zone Selection
Based purely on the principle of relative accommodation, Young’s Moduli and Poisson’s Ratios are conditioned and intuitively processed to grade shale reservoir rock on what matters most: its inability to redirect mechanical force away to neighboring rock. PRY™’can help demystify choosing landing zones and unraveling frac height modeling while its intuitive logic and displays appeal to a wide variety of disciplines and decision makers.