Potential assessment of CO2 source/sink and its matching research during CCS process of deep unworkable seam

Geological background of the study area

Based on regional structural analysis, the Huainan coalfield is located at the southern margin of North China Plate. In the west–east direction, the coal field boundary lies between the Kouziji-Nanzhaoji faults and the Xinchengkou-Changfeng faults. From north to south, the coalfield boundary lies between the Shangtangming-Longshan faults and Yingshang-Dingyuan faults (Fig. 1)^25,26. The coalfield is a near east–west hedge tectonic basin with imbricate fan composed of nappe structures on both sides of the basin and simple synclinic structure in the interior (Fig. 1).

Geological background of Huainan coalfield and distribution of CO₂ source-sink geological points in deep unrecoverable coal seams.

The coal-bearing strata are Taiyuan formation of upper Carboniferous series, Shanxi formation and Xiashihezi formation of lower Permian series, and Shangshihezi formation of upper Permian series, with a total thickness of about 900 m and about 40 layers of coal seams^27,28. In the coal-bearing strata, there are 9–18 coal layers with a single layer thickness greater than 0.7 m on average, the maximum thickness is 12 m, and the total thickness is 23–36 m, which are distributed in Shanxi formation, Xiashihezi formation and lower part of Shangshihezi formation. In this study, the CO₂ emission sources were 10 coal-fired power plants in the coalfield with numbered D1-D10, respectively. Deep unworkable seams are CO₂ storage sinks, which are bounded by faults and numbered B1-B15, respectively (Fig. 1).

Evaluation method of CO₂ geological storage potential

In deep unworkable seam, CO₂ geological storage is mainly in adsorbed, dissolved and free states²⁹, and adsorption storage is the main storage form of coal seam³⁰. Considering the storage differences of different phase of CO₂, the following potential assessment model of CO₂ storage can be adopted^16,31:

where \(M_{{{\text{CO}}_{2} }}\) is CO₂ storage capacity, t; ρ_CO2 is the CO₂ density, kg/m³; M_coal is proved coal reserves, t; m_ab, m_d and m_f are the stored quantity of CO₂ adsorbed, dissolved and free states in coal per unit mass, m³/t.

In the unit mass coal, the storage potential of CO₂ adsorbed state in deep unworkable seam can be characterized by the following formula^16,31:

where P is the reservoir pressure, which is also CO₂ adsorption pressure, MPa; T_c is CO₂ critical temperature, K; Z is the CO₂ compression coefficient; p_c is CO₂ critical pressure, MPa; T is the reservoir temperature, which also CO₂ adsorption temperature, K; and m_ex is the CO₂ excess adsorption amount per unit mass of coal, m³/t, which can be calculated using the following D-R adsorption model^16,31:

where m₀ is the maximum CO₂ adsorption capacity of coal per unit mass tested by adsorption experiment, m³/t; ρ_f and ρ_a are the densities of free and adsorbed CO₂ under the real temperature and pressure conditions, kg/m³; D is the adsorption constant, and k is the constant associated with Henry’s Law.

In coal reservoir, CO₂ density is a function of pressure and temperature, which can be expressed as ρ_f = f(p, T), and can be further characterized as follows^16,31,32:

where δ = ρ_c/ρ_f is the CO₂ reduced density; ρ_c is the CO₂ critical density, kg/m³; τ = T_c/T is the reduced temperature; and ϕ(δ,τ) is the Helmholtz free energy, which can be controlled by temperature and density^16,31,32:

where ϕ^o(δ, τ) is the Helmholtz free energy of ideal fluid, and ϕ^r(δ, τ) is the Helmholtz free energy of the residual fluid.

In deep unworkable seam, the storage potential of dissolved CO₂ per unit mass of coal is a function of coal porosity, water saturation, coal density and CO₂ solubility, which can be characterized as follows^16,31:

where φ is the coal porosity, %; S_w is the water saturation, %; \(S_{{{\text{CO}}_{2} }}\) is the CO₂ solubility, and ρ_coal is the coal density, kg/m³.

According to Boyle-Mariotte law, the free CO₂ storage potential per unit mass of coal in deep unworkable seam can be characterized as follows^16,31:

where S_g is the gas saturation, %; P₀ is the standard atmospheric pressure, MPa; T₀ is the temperature under the standard condition, K; and ρ_visual is the coal apparent density, kg/m³.

Construction of matching model of CO₂ source-sink

CO₂ source and sink matching

CO₂ source-sink matching is the basis of CCUS cluster deployment and its pipe network design and construction, with the goal of minimizing CO₂ transportation cost and maximizing carbon removal. Its essence is the optimization planning of CCUS cluster system^33,34. Based on CO₂ emission source, storage sink, storage geological process, transport network connecting source and sink and corresponding parameter data, the dynamic optimal matching between CO₂ source and sink can be achieved in terms of target quantity, continuity and economic efficiency (Fig. 2).

Schematic diagram of connotation of CO₂ source and sink matching.

The matching of CO₂ source and sink is mainly based on the characteristics of large number, different types and scattered locations of CO₂ emission sources (i.e., thermal power, steel, cement, chemical industry, etc.) and storage sinks (i.e., saltwater layer, CO₂-ECBM, CO₂-EOR, MCO₂-ILU, CO₂-SDR, etc.). Based on the discussion of constraint conditions and determination of objective function, the influence of regional geographical conditions, traffic, population density, transportation cost and transportation mode on CO₂ transport between emission sources and storage sinks is fully considered in the CCUS system. The optimal matching of CO₂ emission sources, storage sinks and transportation parameters was realized, so as to determine scientific and reasonable CO₂ source and sink matching schemes (Fig. 2).

Objective functions

Based on the theory of network analysis in operations research, theoretical models of CO₂ source-sink matching within CCUS technology can be constructed in Huainan coalfield by using the minimum support tree method. The construction of theoretical models should meet the following basic assumptions: (1) Source and sink with the lowest cost should be firstly matched; (2) Allow the matching of one source with multi sinks or one sink with multi sources; (3) Sequestration sink must meet the requirement of CCUS planning period.

In this study, the lowest total cost of matching of CO₂ source-sink in CCS technology is taken as the objective function, namely:

where i refers to the ith CO₂ source; j means the jth CO₂ sink; m indicates the number of CO₂ sources and the value is 10, and n indicates the number of CO₂ sinks with the value of 15.

1. (1)

   CO₂ capture cost (i.e., C_C)

   Based on the analysis of the industrial sources report published by the National Energy Technology Laboratory of the United States, the average capture cost of CO₂ source in coal-fired power plants is $ 64.35 /t^30,35. Therefore, the capture cost of CO₂ source in Huainan coalfield can be characterized as follows:

   $$C_{C} = \sum\limits_{i = 1}^{m} {\sum\limits_{j = 1}^{n} {\omega_{ij} X_{ij} } }$$

   (9)

   where\(\omega_{ij}\) represents the CO₂ capture cost in the i coal-fired power plant, $/t; and X_ij represents CO₂ transport amount from the i coal-fired power plant to the j sequestration sink, t.

2. (2)

   CO₂ transportation cost (i.e., C_T)

   CO₂ transport is most common by pipeline, ship and tanker, and pipeline transportation is suitable for directional transportation with large capacity, long distance and stable load, which mainly includes construction cost and operation and maintenance cost. The operation and maintenance cost accounts for about 1.5% of the construction cost³⁵, which can be calculated according to formula 10 and 11, respectively.

   $$C_{T – j} = 9970 \times \sum\limits_{i = 1}^{m} {\sum\limits_{j = 1}^{n} {L^{1.13} X_{ij}^{0.35} } }$$

   (10)

   where L is the distance of pipeline transportation, km.

   $$C_{T – y} = 0.015N \times 9970 \times \sum\limits_{i = 1}^{m} {\sum\limits_{j = 1}^{n} {L^{1.13} X_{ij}^{0.35} } }$$

   (11)

   where N represents the transportation cycle of the pipeline, year.

   Therefore, CO₂ transport cost can be characterized as follows:

   $$C_{T} = (1 + 0.015N) \times 9970 \times \sum\limits_{i = 1}^{m} {\sum\limits_{j = 1}^{n} {L^{1.13} X_{ij}^{0.35} } }$$

   (12)

3. (3)

   CO₂ sequestration cost (i.e., C_S)

   The cost of CO₂ geological storage is closely related to the amount of CO₂ storage and the type of storage site, and the average storage cost coefficient is $ 5.59 /t^30,35. Therefore, the cost of CO₂ geological storage in coal reservoir can be characterized as follows:

   $$C_{S} = \sum\limits_{i = 1}^{m} {\sum\limits_{j = 1}^{n} {\varepsilon_{ij} X_{ij} } }$$

   (13)

   where\(\varepsilon_{ij}\) is the sequestration cost factor of transporting CO₂ from coal-fired power plant i to sequestration sink j, $/t.

In summary, by substituting formulas (9), (12) and (13) into formula (8), the minimum objective function of total cost of CO₂ source-sink matching in CCS technology can be obtained:

Constraint conditions

Based on the basic assumptions of theoretical model, in the planning process of matching pipe network of CO₂ source-sink with CCS technology, the constraint conditions of the lowest total cost objective function are as follows:

1. (1)

   The total amount of CO₂ captured from all CO₂ emission sources is equal to the total amount of pipeline transport, that is:

   $$a_{i} = \sum\limits_{j = 1}^{n} {X_{ij} }$$

   (15)

   where a_i is the CO₂ capture amount of the ⁱth coal-fired power plant.

2. (2)

   The CO₂ content transported by the pipeline to the storage site shall not exceed the storage capacity of the storage sink, that is:

   $$b_{j} \ge \sum\limits_{i = 1}^{m} {X_{ij} }$$

   (16)

   where b_j is the storage capacity of the j^th storage sink.

3. (3)

   The amount of CO₂ captured in all coal-fired power plants must not exceed the total capacity of all potential sequestration sinks, that is:

   $$\sum\limits_{i = 1}^{m} {a_{i} } \le \sum\limits_{j = 1}^{n} {b_{j} }$$

   (17)

4. (4)

   Non-negative constraint: the pipeline of CO₂ transport content is non-negative, that is:

Optimization of matching pipe network of CO₂ source-sink

The core idea of the mileage saving algorithm is to merge two transportation loops into one loop to reduce the transportation distance in the merging process, and keep cycling until the limit condition is reached, thus reducing the transportation cost. Specifically, three points, A, B and C, transport goods from A to B and C, where the distance from A to B is L_AB (unit: km), the distance from A to C is L_AC (unit: km), and the distance from B to C is L_BC (unit: km), if the transportation from A to B and A to C is separately completed, the transportation distance is 2 × (L_AB + L_AC) with including the round trip process (Fig. 3a). If from A to B, then from B to C, and finally from C back to A, then the transport distance is L_AB + L_AC + L_BC (Fig. 3a), then the distance saved is 2 × (L_AB + L_AC) − (L_AB + L_AC + L_BC) = L_AB + L_AC − L_BC > 0.

Optimization of CCUS source-sink matching pipe network. (a) Traditional mileage saving methods; (b) Improvement of the mileage saving method.

In CO₂ source-sink matching, each sink is taken as the distribution center and distributed with the connected source points. The basic principle is similar to the mileage saving method, except that there is only a transportation network from the source to the sink, and there is no return pipeline. Based on this, the idea of mileage saving method is introduced in this study, and it is improved to meet the needs of CO₂ source-sink matching and transportation network optimization. As shown in Fig. 3b, the CO₂ emitted from points B and C is transported to the storage sink A for storage. The most direct way is from B to A, and then from C to A, with a transport distance of L_AB + L_AC (Fig. 3b). If it is transported from B to C and then from C to A or from C to B and then from B to A (Fig. 3b), the transport distance is L_AC + L_BC or L_AB + L_BC. L_AB and L_AC need to be compared to choose a route with a smaller distance for connection. If L_BC < L_AB/L_AC, then L_AB (L_AC) − L_BC is the savings; if L_BC > L_AB/L_AC, then L_AB/L_AC − L_BC is negative, which means no savings (Fig. 3b).