📄 earth_science

Water Cut Rise Characteristics of Production Wells in Carbonate Reservoirs Based on Production Data Analysis

This study analyzes production data from a Middle Eastern carbonate oilfield to establish normalized water cut rise curves, revealing that reservoir pore structure and fracture development are the primary factors controlling the predominantly "convex" and "convex-S" water cut rise patterns, thereby providing a theoretical basis for optimizing water injection strategies.

Original authors: Fan Lebin, Huang Yinghui, Zhai Guangfu, Zhou Yanbin, Li Depeng, Su Zuofei

Published 2026-07-02

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CC BY 4.0

Original authors: Fan Lebin, Huang Yinghui, Zhai Guangfu, Zhou Yanbin, Li Depeng, Su Zuofei

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine an oilfield as a giant, underground sponge made of rock. In some places, this sponge is like a clean kitchen sponge with tiny, even holes (sandstone). But in the Middle Eastern carbonate reservoirs studied in this paper, the "sponge" is more like a craggy, ancient coral reef. It has big cracks, hidden caves, and uneven holes (fractures and vugs).

The goal of the oil company is to pump oil out of this sponge. To do this, they often push water into the ground to help squeeze the oil toward the production wells. The big problem? Once the water finds a path, it rushes in faster than the oil, and the well starts producing mostly water instead of oil. This is called the "water cut."

Here is what the paper does, explained simply:

1. The Problem: Why Does the Water Rush In?

In these specific rock formations, the water doesn't move evenly. It finds the "super-highways" (big cracks) and zooms through them, leaving the oil behind. This causes the amount of water coming out of the well to spike suddenly. The researchers wanted to understand why some wells flood quickly while others stay dry for a long time.

2. The Solution: A "Fingerprint" for Every Well

Instead of guessing based on rock samples taken from the ground (which often don't tell the whole story of the giant underground field), the authors looked at the actual history of the wells.

Think of it like this: If you want to know how a car drives, you don't just look at the engine parts in a garage; you look at the car's speedometer and fuel gauge over time.

The Method: They took the real production numbers (how much oil and water came out over time) and used math to work backward. They created a unique "fingerprint" (a curve) for each well that shows exactly how the water cut rises as the oil is recovered.
The Result: They found that these fingerprints fall into five distinct shapes, like different types of hills or valleys:
- Convex: A hill that rises fast and then flattens out.
- Convex-S: A hill that starts fast, slows down, then speeds up again.
- S: A smooth, gentle "S" curve.
- Concave-S: A valley shape.
- Concave: A deep, steep drop.

3. The Discovery: What Shapes the Curve?

By looking at the 34 wells they studied, the researchers found that the shape of the curve depends on two main things: the size of the holes in the rock and where the cracks are located.

The "Convex" Shape (The Rush): These wells are usually right next to a big water source or have huge cracks. It's like opening a floodgate; the water rushes in immediately, the water cut spikes fast, and then it levels off.
The "Convex-S" Shape (The Most Common): This was the most common type (13 out of 34 wells). These wells are like a layered cake where the bottom layer is the main producer. The water rises gently at first, then picks up speed, then slows down again. It's a "steady" rise.
The "S" Shape (The Smooth Ride): These wells are in very uniform rock with no big cracks. The water moves evenly, so the water cut rises slowly at first, speeds up in the middle, and slows down at the end. It's the most predictable path.
The "Concave" Shapes (The Late Bloomers): These wells are far from the water source. They produce a lot of oil for a long time with almost no water. But once the water finally breaks through, it hits them like a tsunami, rising very quickly and linearly.

4. Why This Matters

The paper concludes that by looking at the "fingerprint" of a well (the shape of its water curve), engineers can tell a lot about the hidden geology without having to dig more holes.

If the curve looks like a Convex, they know there are big cracks or the well is too close to the water edge.
If it looks like an S, they know the rock is uniform and healthy.

This helps the oil company decide how to push the water. If they know a well is going to flood quickly (Convex), they might change their strategy to slow that water down. If they know a well is stable (S), they can keep pushing harder to get more oil out.

In short: The authors built a tool to read the "story" of a well's water production. By matching that story to the type of rock underground, they can predict how the well will behave and manage the oilfield more efficiently.

Technical Summary: Water Cut Rise Characteristics of Production Wells in Carbonate Reservoirs Based on Production Data Analysis

Problem Statement
In carbonate reservoirs, particularly within Middle Eastern oilfields, production often suffers from rapid water cut increases following breakthrough. This phenomenon, frequently driven by premature water coning and complex flow behaviors, compromises field performance and recovery efficiency. Conventional methods for characterizing water cut rise—relying on core-derived relative permeability curves, waterflood characteristic curves, or adaptations of Tong's chart—often yield indirect estimates that fail to align with the specific, heterogeneous realities of individual wells in carbonate formations. These formations exhibit strong heterogeneity due to dissolution vugs and fractures, rendering standard sandstone models inadequate. Consequently, there is a need for a method to accurately characterize water-cut rise patterns using actual production data to establish a clear link between water-cut trends and subsurface geological features.

Methodology
The study proposes a data-driven approach to calculate individual-well oil-water relative permeability curves directly from production performance data, bypassing the limitations of core experiments. The methodology involves the following steps:

Derivation of Relative Permeability Formulas: The authors utilize standard relative permeability equations for oil ( $K_{ro}$ ) and water ( $K_{rw}$ ) phases, incorporating irreducible water saturation ( $S_{wi}$ ), residual oil saturation ( $S_{or}$ ), and phase exponents ( $\beta$ and $\gamma$ ).
Fitting Relationship: A specific fitting formula is derived to relate the ratio of oil-water relative permeability ( $K_{ro}/K_{rw}$ ) to the recovery factor of recoverable reserves ( $N_p/N_{pf}$ ). This relationship is expressed as:
$\lg(K_{ro}/K_{rw}) = \alpha + \beta \lg(1 - N_p/N_{pf}) + \gamma \lg(N_p/N_{pf})$
Where $\alpha$ , $\beta$ , and $\gamma$ are coefficients to be determined.
Parameter Optimization: Using actual production data (cumulative oil production and water cut) from individual wells, the authors select a suitable data interval from the water cut vs. cumulative oil production curve. The coefficients $\alpha$ , $\beta$ , and $\gamma$ are obtained by fitting this data. The process is iterative; the data interval is adjusted to optimize the fit against historical performance, ensuring the parameters reflect actual well behavior.
Curve Generation: Once the coefficients are determined, the theoretical oil-water relative permeability ratio and theoretical water cut ( $f_w$ ) are calculated to generate the normalized water cut rise characteristic curve (water cut vs. recovery factor from 0 to 1).

Key Contributions and Results
The methodology was applied to 34 production wells in a target Middle Eastern carbonate oilfield. The study achieved the following:

Classification of Water Cut Rise Patterns: The analysis identified five distinct water cut rise curve types based on shape: "Convex," "Convex-S," "S," "Concave-S," and "Concave."
Statistical Dominance: The "Convex-S" shape was found to be the predominant characteristic in the field, accounting for 13 of the 34 wells. These wells typically feature multi-layer commingled production where the lowest perforated zone is the main contributor, resembling a positive rhythmic sequence, resulting in a relatively gentle water cut rise.
Geological Correlation: The study established a direct link between curve morphology and geological/reservoir factors:
- "Convex" curves (6 wells): Associated with wells near edge/bottom water where water rapidly cones along fractures and high-permeability layers.
- "S" curves (7 wells): Linked to reservoirs with good homogeneity (low permeability variation coefficient).
- "Concave-S" and "Concave" curves: Associated with structural highs or flanks near edge water, characterized by fractured reservoirs with single contributing layers. These often show high recovery factors during the water-free period followed by rapid water cut increases post-breakthrough.
Primary Controlling Factors: The analysis concludes that while rock wettability and viscosity ratios are fundamental, the water cut rise characteristics in this specific carbonate field are primarily controlled by rock pore structure (intergranular/intercrystalline pores and fractures) and reservoir heterogeneity (vertical lithology and fracture distribution).

Significance
The paper claims that this research provides a theoretical basis for optimizing water injection schemes and improving oil recovery efficiency in carbonate reservoirs. By establishing a relationship between water cut rise patterns and subsurface geological conditions through a method grounded in actual production data, the study offers a tool to better evaluate reservoir performance. The findings allow for a more accurate prediction of water cut behavior tailored to specific well geological settings, addressing the inadequacy of conventional models for heterogeneous carbonate systems.

1. The Problem: Why Does the Water Rush In?

2. The Solution: A "Fingerprint" for Every Well

3. The Discovery: What Shapes the Curve?

4. Why This Matters

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